query: As a data analyst working with high-dimensional time series data, I am seeking to develop a model capable of handling large datasets, even those with missing values. The model should be able to process data online efficiently, representing high-dimensional data using low-dimensional embeddings without any loss of information. My goal is to identify a method that deconstructs embeddings of large data into simpler components, thereby facilitating efficient learning while simultaneously adjusting to new information online. Subsequently, I aim to train a model that forecasts variables' values based on their previous embeddings. I am particularly interested in testing this method on large, real-world datasets to evaluate its prediction accuracy. Ultimately, my objective is to utilize this model for predicting trends in sectors such as finance, energy, and transportation, especially when dealing with complex time series data.

positive:
In Internet of things (IoT), data is continuously recorded from different
data sources and devices can suffer faults in their embedded electronics, thus
leading to a high-dimensional data sets and concept drift events. Therefore,
methods that are capable of high-dimensional non-stationary time series are of
great value in IoT applications. Fuzzy Time Series (FTS) models stand out as
data-driven non-parametric models of easy implementation and high accuracy.
Unfortunately, FTS encounters difficulties when dealing with data sets of many
variables and scenarios with concept drift. We present a new approach to handle
high-dimensional non-stationary time series, by projecting the original
high-dimensional data into a low dimensional embedding space and using FTS
approach. Combining these techniques enables a better representation of the
complex content of non-stationary multivariate time series and accurate
forecasts. Our model is able to explain 98% of the variance and reach 11.52% of
RMSE, 2.68% of MAE and 2.91% of MAPE.

positive:
Efficient processing of large-scale time series data is an intricate problem
in machine learning. Conventional sensor signal processing pipelines with hand
engineered feature extraction often involve huge computational cost with high
dimensional data. Deep recurrent neural networks have shown promise in
automated feature learning for improved time-series processing. However,
generic deep recurrent models grow in scale and depth with increased complexity
of the data. This is particularly challenging in presence of high dimensional
data with temporal and spatial characteristics. Consequently, this work
proposes a novel deep cellular recurrent neural network (DCRNN) architecture to
efficiently process complex multi-dimensional time series data with spatial
information. The cellular recurrent architecture in the proposed model allows
for location-aware synchronous processing of time series data from spatially
distributed sensor signal sources. Extensive trainable parameter sharing due to
cellularity in the proposed architecture ensures efficiency in the use of
recurrent processing units with high-dimensional inputs. This study also
investigates the versatility of the proposed DCRNN model for classification of
multi-class time series data from different application domains. Consequently,
the proposed DCRNN architecture is evaluated using two time-series datasets: a
multichannel scalp EEG dataset for seizure detection, and a machine fault
detection dataset obtained in-house. The results suggest that the proposed
architecture achieves state-of-the-art performance while utilizing
substantially less trainable parameters when compared to comparable methods in
the literature.

positive:
The prediction of residential power usage is essential in assisting a smart
grid to manage and preserve energy to ensure efficient use. An accurate energy
forecasting at the customer level will reflect directly into efficiency
improvements across the power grid system, however forecasting building energy
use is a complex task due to many influencing factors, such as meteorological
and occupancy patterns. In addiction, high-dimensional time series increasingly
arise in the Internet of Energy (IoE), given the emergence of multi-sensor
environments and the two way communication between energy consumers and the
smart grid. Therefore, methods that are capable of computing high-dimensional
time series are of great value in smart building and IoE applications. Fuzzy
Time Series (FTS) models stand out as data-driven non-parametric models of easy
implementation and high accuracy. Unfortunately, the existing FTS models can be
unfeasible if all features were used to train the model. We present a new
methodology for handling high-dimensional time series, by projecting the
original high-dimensional data into a low dimensional embedding space and using
multivariate FTS approach in this low dimensional representation. Combining
these techniques enables a better representation of the complex content of
multivariate time series and more accurate forecasts.

positive:
Learning dynamical models from data plays a vital role in engineering design,
optimization, and predictions. Building models describing dynamics of complex
processes (e.g., weather dynamics, or reactive flows) using empirical knowledge
or first principles are onerous or infeasible. Moreover, these models are
high-dimensional but spatially correlated. It is, however, observed that the
dynamics of high-fidelity models often evolve in low-dimensional manifolds.
Furthermore, it is also known that for sufficiently smooth vector fields
defining the nonlinear dynamics, a quadratic model can describe it accurately
in an appropriate coordinate system, conferring to the McCormick relaxation
idea in nonconvex optimization. Here, we aim at finding a low-dimensional
embedding of high-fidelity dynamical data, ensuring a simple quadratic model to
explain its dynamics. To that aim, this work leverages deep learning to
identify low-dimensional quadratic embeddings for high-fidelity dynamical
systems. Precisely, we identify the embedding of data using an autoencoder to
have the desired property of the embedding. We also embed a Runge-Kutta method
to avoid the time-derivative computations, which is often a challenge. We
illustrate the ability of the approach by a couple of examples, arising in
describing flow dynamics and the oscillatory tubular reactor model.

positive:
Spatial time series forecasting problems arise in a broad range of
applications, such as environmental and transportation problems. These problems
are challenging because of the existence of specific spatial, short-term and
long-term patterns, and the curse of dimensionality. In this paper, we propose
a deep neural network framework for large-scale spatial time series forecasting
problems. We explicitly designed the neural network architecture for capturing
various types of patterns. In preprocessing, a time series decomposition method
is applied to separately feed short-term, long-term and spatial patterns into
different components of a neural network. A fuzzy clustering method finds
cluster of neighboring time series based on similarity of time series
residuals; as they can be meaningful short-term patterns for spatial time
series. In neural network architecture, each kernel of a multi-kernel
convolution layer is applied to a cluster of time series to extract short-term
features in neighboring areas. The output of convolution layer is concatenated
by trends and followed by convolution-LSTM layer to capture long-term patterns
in larger regional areas. To make a robust prediction when faced with missing
data, an unsupervised pretrained denoising autoencoder reconstructs the output
of the model in a fine-tuning step. The experimental results illustrate the
model outperforms baseline and state of the art models in a traffic flow
prediction dataset.

positive:
Machine learning and in particular deep learning algorithms are the emerging
approaches to data analysis. These techniques have transformed traditional data
mining-based analysis radically into a learning-based model in which existing
data sets along with their cluster labels (i.e., train set) are learned to
build a supervised learning model and predict the cluster labels of unseen data
(i.e., test set). In particular, deep learning techniques are capable of
capturing and learning hidden features in a given data sets and thus building a
more accurate prediction model for clustering and labeling problem. However,
the major problem is that time series data are often unlabeled and thus
supervised learning-based deep learning algorithms cannot be directly adapted
to solve the clustering problems for these special and complex types of data
sets. To address this problem, this paper introduces a two-stage method for
clustering time series data. First, a novel technique is introduced to utilize
the characteristics (e.g., volatility) of given time series data in order to
create labels and thus be able to transform the problem from unsupervised
learning into supervised learning. Second, an autoencoder-based deep learning
model is built to learn and model both known and hidden features of time series
data along with their created labels to predict the labels of unseen time
series data. The paper reports a case study in which financial and stock time
series data of selected 70 stock indices are clustered into distinct groups
using the introduced two-stage procedure. The results show that the proposed
procedure is capable of achieving 87.5\% accuracy in clustering and predicting
the labels for unseen time series data.

positive:
We propose a three-stage framework for forecasting high-dimensional
time-series data. Our method first estimates parameters for each univariate
time series. Next, we use these parameters to cluster the time series. These
clusters can be viewed as multivariate time series, for which we then compute
parameters. The forecasted values of a single time series can depend on the
history of other time series in the same cluster, accounting for intra-cluster
similarity while minimizing potential noise in predictions by ignoring
inter-cluster effects. Our framework -- which we refer to as
"cluster-and-conquer" -- is highly general, allowing for any time-series
forecasting and clustering method to be used in each step. It is
computationally efficient and embarrassingly parallel. We motivate our
framework with a theoretical analysis in an idealized mixed linear regression
setting, where we provide guarantees on the quality of the estimates. We
accompany these guarantees with experimental results that demonstrate the
advantages of our framework: when instantiated with simple linear
autoregressive models, we are able to achieve state-of-the-art results on
several benchmark datasets, sometimes outperforming deep-learning-based
approaches.

positive:
Informative missingness is unavoidable in the digital processing of
continuous time series, where the value for one or more observations at
different time points are missing. Such missing observations are one of the
major limitations of time series processing using deep learning. Practical
applications, e.g., sensor data, healthcare, weather, generates data that is in
truth continuous in time, and informative missingness is a common phenomenon in
these datasets. These datasets often consist of multiple variables, and often
there are missing values for one or many of these variables. This
characteristic makes time series prediction more challenging, and the impact of
missing input observations on the accuracy of the final output can be
significant. A recent novel deep learning model called GRU-D is one early
attempt to address informative missingness in time series data. On the other
hand, a new family of neural networks called Neural ODEs (Ordinary Differential
Equations) are natural and efficient for processing time series data which is
continuous in time. In this paper, a deep learning model is proposed that
leverages the effective imputation of GRU-D, and the temporal continuity of
Neural ODEs. A time series classification task performed on the PhysioNet
dataset demonstrates the performance of this architecture.

positive:
Recent advances in matrix completion enable data imputation in full-rank
matrices by exploiting low dimensional (nonlinear) latent structure. In this
paper, we develop a new model for high rank matrix completion (HRMC), together
with batch and online methods to fit the model and out-of-sample extension to
complete new data. The method works by (implicitly) mapping the data into a
high dimensional polynomial feature space using the kernel trick; importantly,
the data occupies a low dimensional subspace in this feature space, even when
the original data matrix is of full-rank. We introduce an explicit
parametrization of this low dimensional subspace, and an online fitting
procedure, to reduce computational complexity compared to the state of the art.
The online method can also handle streaming or sequential data and adapt to
non-stationary latent structure. We provide guidance on the sampling rate
required these methods to succeed. Experimental results on synthetic data and
motion capture data validate the performance of the proposed methods.

positive:
Non-linear manifold learning enables high-dimensional data analysis, but
requires out-of-sample-extension methods to process new data points. In this
paper, we propose a manifold learning algorithm based on deep learning to
create an encoder, which maps a high-dimensional dataset and its
low-dimensional embedding, and a decoder, which takes the embedded data back to
the high-dimensional space. Stacking the encoder and decoder together
constructs an autoencoder, which we term a diffusion net, that performs
out-of-sample-extension as well as outlier detection. We introduce new neural
net constraints for the encoder, which preserves the local geometry of the
points, and we prove rates of convergence for the encoder. Also, our approach
is efficient in both computational complexity and memory requirements, as
opposed to previous methods that require storage of all training points in both
the high-dimensional and the low-dimensional spaces to calculate the
out-of-sample-extension and the pre-image.

positive:
Large-scale and multidimensional spatiotemporal data sets are becoming
ubiquitous in many real-world applications such as monitoring urban traffic and
air quality. Making predictions on these time series has become a critical
challenge due to not only the large-scale and high-dimensional nature but also
the considerable amount of missing data. In this paper, we propose a Bayesian
temporal factorization (BTF) framework for modeling multidimensional time
series -- in particular spatiotemporal data -- in the presence of missing
values. By integrating low-rank matrix/tensor factorization and vector
autoregressive (VAR) process into a single probabilistic graphical model, this
framework can characterize both global and local consistencies in large-scale
time series data. The graphical model allows us to effectively perform
probabilistic predictions and produce uncertainty estimates without imputing
those missing values. We develop efficient Gibbs sampling algorithms for model
inference and model updating for real-time prediction and test the proposed BTF
framework on several real-world spatiotemporal data sets for both missing data
imputation and multi-step rolling prediction tasks. The numerical experiments
demonstrate the superiority of the proposed BTF approaches over existing
state-of-the-art methods.

positive:
This paper proposes a probabilistic neural network developed on the basis of
time-series discriminant component analysis (TSDCA) that can be used to
classify high-dimensional time-series patterns. TSDCA involves the compression
of high-dimensional time series into a lower-dimensional space using a set of
orthogonal transformations and the calculation of posterior probabilities based
on a continuous-density hidden Markov model with a Gaussian mixture model
expressed in the reduced-dimensional space. The analysis can be incorporated
into a neural network, which is named a time-series discriminant component
network (TSDCN), so that parameters of dimensionality reduction and
classification can be obtained simultaneously as network coefficients according
to a backpropagation through time-based learning algorithm with the Lagrange
multiplier method. The TSDCN is considered to enable high-accuracy
classification of high-dimensional time-series patterns and to reduce the
computation time taken for network training. The validity of the TSDCN is
demonstrated for high-dimensional artificial data and EEG signals in the
experiments conducted during the study.

positive:
Time series data account for a major part of data supply available today.
Time series mining handles several tasks such as classification, clustering,
query-by-content, prediction, and others. Performing data mining tasks on raw
time series is inefficient as these data are high-dimensional by nature.
Instead, time series are first pre-processed using several techniques before
different data mining tasks can be performed on them. In general, there are two
main approaches to reduce time series dimensionality, the first is what we call
landmark methods. These methods are based on finding characteristic features in
the target time series. The second is based on data transformations. These
methods transform the time series from the original space into a reduced space,
where they can be managed more efficiently. The method we present in this paper
applies a third approach, as it projects a time series onto a lower-dimensional
space by selecting important points in the time series. The novelty of our
method is that these points are not chosen according to a geometric criterion,
which is subjective in most cases, but through an optimization process. The
other important characteristic of our method is that these important points are
selected on a dataset-level and not on a single time series-level. The direct
advantage of this strategy is that the distance defined on the low-dimensional
space lower bounds the original distance applied to raw data. This enables us
to apply the popular GEMINI algorithm. The promising results of our experiments
on a wide variety of time series datasets, using different optimizers, and
applied to the two major data mining tasks, validate our new method.

positive:
Multivariate time series forecasting is of great importance to many
scientific disciplines and industrial sectors. The evolution of a multivariate
time series depends on the dynamics of its variables and the connectivity
network of causal interrelationships among them. Most of the existing time
series models do not account for the causal effects among the system's
variables and even if they do they rely just on determining the
between-variables causality network. Knowing the structure of such a complex
network and even more specifically knowing the exact lagged variables that
contribute to the underlying process is crucial for the task of multivariate
time series forecasting. The latter is a rather unexplored source of
information to leverage. In this direction, here a novel neural network-based
architecture is proposed, termed LAgged VAriable Representation NETwork
(LAVARNET), which intrinsically estimates the importance of lagged variables
and combines high dimensional latent representations of them to predict future
values of time series. Our model is compared with other baseline and state of
the art neural network architectures on one simulated data set and four real
data sets from meteorology, music, solar activity, and finance areas. The
proposed architecture outperforms the competitive architectures in most of the
experiments.

positive:
We propose a novel probabilistic dimensionality reduction framework that can
naturally integrate the generative model and the locality information of data.
Based on this framework, we present a new model, which is able to learn a
smooth skeleton of embedding points in a low-dimensional space from
high-dimensional noisy data. The formulation of the new model can be
equivalently interpreted as two coupled learning problem, i.e., structure
learning and the learning of projection matrix. This interpretation motivates
the learning of the embedding points that can directly form an explicit graph
structure. We develop a new method to learn the embedding points that form a
spanning tree, which is further extended to obtain a discriminative and compact
feature representation for clustering problems. Unlike traditional clustering
methods, we assume that centers of clusters should be close to each other if
they are connected in a learned graph, and other cluster centers should be
distant. This can greatly facilitate data visualization and scientific
discovery in downstream analysis. Extensive experiments are performed that
demonstrate that the proposed framework is able to obtain discriminative
feature representations, and correctly recover the intrinsic structures of
various real-world datasets.

positive:
The imputation of missing values in time series has many applications in
healthcare and finance. While autoregressive models are natural candidates for
time series imputation, score-based diffusion models have recently outperformed
existing counterparts including autoregressive models in many tasks such as
image generation and audio synthesis, and would be promising for time series
imputation. In this paper, we propose Conditional Score-based Diffusion models
for Imputation (CSDI), a novel time series imputation method that utilizes
score-based diffusion models conditioned on observed data. Unlike existing
score-based approaches, the conditional diffusion model is explicitly trained
for imputation and can exploit correlations between observed values. On
healthcare and environmental data, CSDI improves by 40-65% over existing
probabilistic imputation methods on popular performance metrics. In addition,
deterministic imputation by CSDI reduces the error by 5-20% compared to the
state-of-the-art deterministic imputation methods. Furthermore, CSDI can also
be applied to time series interpolation and probabilistic forecasting, and is
competitive with existing baselines. The code is available at
https://github.com/ermongroup/CSDI.

negative:
The high-dimensional data setting, in which p >> n, is a challenging
statistical paradigm that appears in many real-world problems. In this setting,
learning a compact, low-dimensional representation of the data can
substantially help distinguish signal from noise. One way to achieve this goal
is to perform subspace learning to estimate a small set of latent features that
capture the majority of the variance in the original data. Most existing
subspace learning models, such as PCA, assume that the data can be fully
represented by its embedding in one or more latent subspaces. However, in this
work, we argue that this assumption is not suitable for many high-dimensional
datasets; often only some variables can easily be projected to a
low-dimensional space. We propose a hybrid dimensionality reduction technique
in which some features are mapped to a low-dimensional subspace while others
remain in the original space. Our model leads to more accurate estimation of
the latent space and lower reconstruction error. We present a simple
optimization procedure for the resulting biconvex problem and show synthetic
data results that demonstrate the advantages of our approach over existing
methods. Finally, we demonstrate the effectiveness of this method for
extracting meaningful features from both gene expression and video background
subtraction datasets.

negative:
Identifying systems with high-dimensional inputs and outputs, such as systems
measured by video streams, is a challenging problem with numerous applications
in robotics, autonomous vehicles and medical imaging. In this paper, we propose
a novel non-linear state-space identification method starting from
high-dimensional input and output data. Multiple computational and conceptual
advances are combined to handle the high-dimensional nature of the data. An
encoder function, represented by a neural network, is introduced to learn a
reconstructability map to estimate the model states from past inputs and
outputs. This encoder function is jointly learned with the dynamics.
Furthermore, multiple computational improvements, such as an improved
reformulation of multiple shooting and batch optimization, are proposed to keep
the computational time under control when dealing with high-dimensional and
large datasets. We apply the proposed method to a video stream of a simulated
environment of a controllable ball in a unit box. The study shows low
simulation error with excellent long term prediction capability of the model
obtained using the proposed method.

negative:
In the era of big data, practical applications in various domains continually
generate large-scale time-series data. Among them, some data show significant
or potential periodicity characteristics, such as meteorological and financial
data. It is critical to efficiently identify the potential periodic patterns
from massive time-series data and provide accurate predictions. In this paper,
a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for
large-scale time-series data is proposed and implemented in the Apache Spark
cloud computing environment. To effectively handle the massive historical
datasets, a Time Series Data Compression and Abstraction (TSDCA) algorithm is
presented, which can reduce the data scale as well as accurately extracting the
characteristics. Based on this, we propose a Multi-layer Time Series Periodic
Pattern Recognition (MTSPPR) algorithm using the Fourier Spectrum Analysis
(FSA) method. In addition, a Periodicity-based Time Series Prediction (PTSP)
algorithm is proposed. Data in the subsequent period are predicted based on all
previous period models, in which a time attenuation factor is introduced to
control the impact of different periods on the prediction results. Moreover, to
improve the performance of the proposed algorithms, we propose a parallel
solution on the Apache Spark platform, using the Streaming real-time computing
module. To efficiently process the large-scale time-series datasets in
distributed computing environments, Distributed Streams (DStreams) and
Resilient Distributed Datasets (RDDs) are used to store and calculate these
datasets. Extensive experimental results show that our PPTSP algorithm has
significant advantages compared with other algorithms in terms of prediction
accuracy and performance.

negative:
Multivariate time-series data are used in many classification and regression
predictive tasks, and recurrent models have been widely used for such tasks.
Most common recurrent models assume that time-series data elements are of equal
length and the ordered observations are recorded at regular intervals. However,
real-world time-series data have neither a similar length nor a same number of
observations. They also have missing entries, which hinders the performance of
predictive tasks. In this paper, we approach these issues by presenting a model
for the combined task of imputing and predicting values for the irregularly
observed and varying length time-series data with missing entries. Our proposed
model (Bi-GAN) uses a bidirectional recurrent network in a generative
adversarial setting. The generator is a bidirectional recurrent network that
receives actual incomplete data and imputes the missing values. The
discriminator attempts to discriminate between the actual and the imputed
values in the output of the generator. Our model learns how to impute missing
elements in-between (imputation) or outside of the input time steps
(prediction), hence working as an effective any-time prediction tool for
time-series data. Our method has three advantages to the state-of-the-art
methods in the field: (a) single model can be used for both imputation and
prediction tasks; (b) it can perform prediction task for time-series of varying
length with missing data; (c) it does not require to know the observation and
prediction time window during training which provides a flexible length of
prediction window for both long-term and short-term predictions. We evaluate
our model on two public datasets and on another large real-world electronic
health records dataset to impute and predict body mass index (BMI) values in
children and show its superior performance in both settings.

negative:
Many areas of research are characterised by the deluge of large-scale
highly-dimensional time-series data. However, using the data available for
prediction and decision making is hampered by the current lag in our ability to
uncover and quantify true interactions that explain the outcomes.We are
interested in areas such as intensive care medicine, which are characterised by
i) continuous monitoring of multivariate variables and non-uniform sampling of
data streams, ii) the outcomes are generally governed by interactions between a
small set of rare events, iii) these interactions are not necessarily definable
by specific values (or value ranges) of a given group of variables, but rather,
by the deviations of these values from the normal state recorded over time, iv)
the need to explain the predictions made by the model. Here, while numerous
data mining models have been formulated for outcome prediction, they are unable
to explain their predictions.
  We present a model for uncovering interactions with the highest likelihood of
generating the outcomes seen from highly-dimensional time series data.
Interactions among variables are represented by a relational graph structure,
which relies on qualitative abstractions to overcome non-uniform sampling and
to capture the semantics of the interactions corresponding to the changes and
deviations from normality of variables of interest over time. Using the
assumption that similar templates of small interactions are responsible for the
outcomes (as prevalent in the medical domains), we reformulate the discovery
task to retrieve the most-likely templates from the data.

negative:
Extracting previously unknown patterns and information in time series is
central to many real-world applications. In this study, we introduce a novel
approach to modeling financial time series using a deep learning model. We use
a Long Short-Term Memory (LSTM) network equipped with the trainable initial
hidden states. By learning to reconstruct time series, the proposed model can
represent high-dimensional time series data with its parameters. An experiment
with the Korean stock market data showed that the model was able to capture the
relative similarity between a large number of stock prices in its latent space.
Besides, the model was also able to predict the future stock trends from the
latent space. The proposed method can help to identify relationships among many
time series, and it could be applied to financial applications, such as
optimizing the investment portfolios.

negative:
Time series data generation has drawn increasing attention in recent years.
Several generative adversarial network (GAN) based methods have been proposed
to tackle the problem usually with the assumption that the targeted time series
data are well-formatted and complete. However, real-world time series (RTS)
data are far away from this utopia, e.g., long sequences with variable lengths
and informative missing data raise intractable challenges for designing
powerful generation algorithms. In this paper, we propose a novel generative
framework for RTS data - RTSGAN to tackle the aforementioned challenges. RTSGAN
first learns an encoder-decoder module which provides a mapping between a time
series instance and a fixed-dimension latent vector and then learns a
generation module to generate vectors in the same latent space. By combining
the generator and the decoder, RTSGAN is able to generate RTS which respect the
original feature distributions and the temporal dynamics. To generate time
series with missing values, we further equip RTSGAN with an observation
embedding layer and a decide-and-generate decoder to better utilize the
informative missing patterns. Experiments on the four RTS datasets show that
the proposed framework outperforms the previous generation methods in terms of
synthetic data utility for downstream classification and prediction tasks.

negative:
In the current context of Big Data, the nature of many forecasting problems
has changed from predicting isolated time series to predicting many time series
from similar sources. This has opened up the opportunity to develop competitive
global forecasting models that simultaneously learn from many time series. But,
it still remains unclear when global forecasting models can outperform the
univariate benchmarks, especially along the dimensions of the
homogeneity/heterogeneity of series, the complexity of patterns in the series,
the complexity of forecasting models, and the lengths/number of series. Our
study attempts to address this problem through investigating the effect from
these factors, by simulating a number of datasets that have controllable time
series characteristics. Specifically, we simulate time series from simple data
generating processes (DGP), such as Auto Regressive (AR) and Seasonal AR, to
complex DGPs, such as Chaotic Logistic Map, Self-Exciting Threshold
Auto-Regressive, and Mackey-Glass Equations. The data heterogeneity is
introduced by mixing time series generated from several DGPs into a single
dataset. The lengths and the number of series in the dataset are varied in
different scenarios. We perform experiments on these datasets using global
forecasting models including Recurrent Neural Networks (RNN), Feed-Forward
Neural Networks, Pooled Regression (PR) models and Light Gradient Boosting
Models (LGBM), and compare their performance against standard statistical
univariate forecasting techniques. Our experiments demonstrate that when
trained as global forecasting models, techniques such as RNNs and LGBMs, which
have complex non-linear modelling capabilities, are competitive methods in
general under challenging forecasting scenarios such as series having short
lengths, datasets with heterogeneous series and having minimal prior knowledge
of the patterns of the series.

negative:
More than 80% of today's data is unstructured in nature, and these
unstructured datasets evolve over time. A large part of these datasets are text
documents generated by media outlets, scholarly articles in digital libraries,
findings from scientific and professional communities, and social media. Vector
space models were developed to analyze text data using data mining and machine
learning algorithms. While ample vector space models exist for text data, the
evolutionary aspect of ever-changing text corpora is still missing in
vector-based representations. The advent of word embeddings has enabled us to
create a contextual vector space, but the embeddings fail to consider the
temporal aspects of the feature space successfully. This paper presents an
approach to include temporal aspects in feature spaces. The inclusion of the
time aspect in the feature space provides vectors for every natural language
element, such as words or entities, at every timestamp. Such temporal word
vectors allow us to track how the meaning of a word changes over time, by
studying the changes in its neighborhood. Moreover, a time-reflective text
representation will pave the way to a new set of text analytic abilities
involving time series for text collections. In this paper, we present a
time-reflective vector space model for temporal text data that is able to
capture short and long-term changes in the meaning of words. We compare our
approach with the limited literature on dynamic embeddings. We present
qualitative and quantitative evaluations using the tracking of semantic
evolution as the target application.

negative:
Time series forecasting is often fundamental to scientific and engineering
problems and enables decision making. With ever increasing data set sizes, a
trivial solution to scale up predictions is to assume independence between
interacting time series. However, modeling statistical dependencies can improve
accuracy and enable analysis of interaction effects. Deep learning methods are
well suited for this problem, but multivariate models often assume a simple
parametric distribution and do not scale to high dimensions. In this work we
model the multivariate temporal dynamics of time series via an autoregressive
deep learning model, where the data distribution is represented by a
conditioned normalizing flow. This combination retains the power of
autoregressive models, such as good performance in extrapolation into the
future, with the flexibility of flows as a general purpose high-dimensional
distribution model, while remaining computationally tractable. We show that it
improves over the state-of-the-art for standard metrics on many real-world data
sets with several thousand interacting time-series.

negative:
The prediction of high-resolution hourly traffic volumes of a given roadway
is essential for transportation planning. Traditionally, Automatic Traffic
Recorders (ATR) are used to collect this hourly volume data. These large
datasets are time series data characterized by long-term temporal dependencies
and missing values. Regarding the temporal dependencies, all roadways are
characterized by seasonal variations that can be weekly, monthly or yearly,
depending on the cause of the variation. Regarding the missing data in a
time-series sequence, traditional time series forecasting models perform poorly
under the influence of seasonal variations. To address this limitation, robust,
Recurrent Neural Network (RNN) based, multi-step ahead forecasting models are
developed for time-series in this study. The simple RNN, the Gated Recurrent
Unit (GRU) and the Long Short-Term Memory (LSTM) units are used to develop the
model and evaluate its performance. Two approaches are used to address the
missing value issue: masking and imputation, in conjunction with the RNN
models. Six different imputation algorithms are then used to identify the best
model. The analysis indicates that the LSTM model performs better than simple
RNN and GRU models, and imputation performs better than masking to predict
future traffic volume. Based on analysis using 92 ATRs, the LSTM-Median model
is deemed the best model in all scenarios for hourly traffic volume and AADT
prediction, with an average RMSE of 274 and MAPE of 18.91% for hourly traffic
volume prediction and average RMSE of 824 and MAPE of 2.10% for AADT
prediction.

negative:
Forecasting with multivariate time series, which aims to predict future
values given previous and current several univariate time series data, has been
studied for decades, with one example being ARIMA. Because it is difficult to
measure the extent to which noise is mixed with informative signals within
rapidly fluctuating financial time series data, designing a good predictive
model is not a simple task. Recently, many researchers have become interested
in recurrent neural networks and attention-based neural networks, applying them
in financial forecasting. There have been many attempts to utilize these
methods for the capturing of long-term temporal dependencies and to select more
important features in multivariate time series data in order to make accurate
predictions. In this paper, we propose a new prediction framework based on deep
neural networks and a trend filtering, which converts noisy time series data
into a piecewise linear fashion. We reveal that the predictive performance of
deep temporal neural networks improves when the training data is temporally
processed by a trend filtering. To verify the effect of our framework, three
deep temporal neural networks, state of the art models for predictions in time
series finance data, are used and compared with models that contain trend
filtering as an input feature. Extensive experiments on real-world multivariate
time series data show that the proposed method is effective and significantly
better than existing baseline methods.

negative:
We present a novel and scalable label embedding framework for large-scale
multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using
Distributional Semantics). Our approach draws inspiration from ideas rooted in
distributional semantics, specifically the Skip Gram Negative Sampling (SGNS)
approach, widely used to learn word embeddings for natural language processing
tasks. Learning such embeddings can be reduced to a certain matrix
factorization. Our approach is novel in that it highlights interesting
connections between label embedding methods used for multi-label learning and
paragraph/document embedding methods commonly used for learning representations
of text data. The framework can also be easily extended to incorporate
auxiliary information such as label-label correlations; this is crucial
especially when there are a lot of missing labels in the training data. We
demonstrate the effectiveness of our approach through an extensive set of
experiments on a variety of benchmark datasets, and show that the proposed
learning methods perform favorably compared to several baselines and
state-of-the-art methods for large-scale multi-label learning. To facilitate
end-to-end learning, we develop a joint learning algorithm that can learn the
embeddings as well as a regression model that predicts these embeddings given
input features, via efficient gradient-based methods.

negative:
Feature-based time series representations have attracted substantial
attention in a wide range of time series analysis methods. Recently, the use of
time series features for forecast model averaging has been an emerging research
focus in the forecasting community. Nonetheless, most of the existing
approaches depend on the manual choice of an appropriate set of features.
Exploiting machine learning methods to extract features from time series
automatically becomes crucial in state-of-the-art time series analysis. In this
paper, we introduce an automated approach to extract time series features based
on time series imaging. We first transform time series into recurrence plots,
from which local features can be extracted using computer vision algorithms.
The extracted features are used for forecast model averaging. Our experiments
show that forecasting based on automatically extracted features, with less
human intervention and a more comprehensive view of the raw time series data,
yields highly comparable performances with the best methods in the largest
forecasting competition dataset (M4) and outperforms the top methods in the
Tourism forecasting competition dataset.

negative:
Time series forecasting is an important predictive methodology which can be
applied to a wide range of problems. Particularly, forecasting the indoor
temperature permits an improved utilization of the HVAC (Heating, Ventilating
and Air Conditioning) systems in a home and thus a better energy efficiency.
With such purpose the paper describes how to implement an Artificial Neural
Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous
intelligent wireless sensor network. The present paper uses a Wireless Sensor
Networks (WSN) to monitor and forecast the indoor temperature in a smart home,
based on low resources and cost microcontroller technology as the 8051MCU. An
on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs,
has been developed for real-time time series learning. It performs the model
training with every new data that arrive to the system, without saving enormous
quantities of data to create a historical database as usual, i.e., without
previous knowledge. Consequently to validate the approach a simulation study
through a Bayesian baseline model have been tested in order to compare with a
database of a real application aiming to see the performance and accuracy. The
core of the paper is a new algorithm, based on the BP one, which has been
described in detail, and the challenge was how to implement a computational
demanding algorithm in a simple architecture with very few hardware resources.

negative:
In many applications of finance, biology and sociology, complex systems
involve entities interacting with each other. These processes have the
peculiarity of evolving over time and of comprising latent factors, which
influence the system without being explicitly measured. In this work we present
latent variable time-varying graphical lasso (LTGL), a method for multivariate
time-series graphical modelling that considers the influence of hidden or
unmeasurable factors. The estimation of the contribution of the latent factors
is embedded in the model which produces both sparse and low-rank components for
each time point. In particular, the first component represents the connectivity
structure of observable variables of the system, while the second represents
the influence of hidden factors, assumed to be few with respect to the observed
variables. Our model includes temporal consistency on both components,
providing an accurate evolutionary pattern of the system. We derive a tractable
optimisation algorithm based on alternating direction method of multipliers,
and develop a scalable and efficient implementation which exploits proximity
operators in closed form. LTGL is extensively validated on synthetic data,
achieving optimal performance in terms of accuracy, structure learning and
scalability with respect to ground truth and state-of-the-art methods for
graphical inference. We conclude with the application of LTGL to real case
studies, from biology and finance, to illustrate how our method can be
successfully employed to gain insights on multivariate time-series data.

negative:
Sequential and temporal data arise in many fields of research, such as
quantitative finance, medicine, or computer vision. A novel approach for
sequential learning, called the signature method and rooted in rough path
theory, is considered. Its basic principle is to represent multidimensional
paths by a graded feature set of their iterated integrals, called the
signature. This approach relies critically on an embedding principle, which
consists in representing discretely sampled data as paths, i.e., functions from
$[0,1]$ to $\mathbb{R}^d$. After a survey of machine learning methodologies for
signatures, the influence of embeddings on prediction accuracy is investigated
with an in-depth study of three recent and challenging datasets. It is shown
that a specific embedding, called lead-lag, is systematically the strongest
performer across all datasets and algorithms considered. Moreover, an empirical
study reveals that computing signatures over the whole path domain does not
lead to a loss of local information. It is concluded that, with a good
embedding, combining signatures with other simple algorithms achieves results
competitive with state-of-the-art, domain-specific approaches.

negative:
Forecasting models that are trained across sets of many time series, known as
Global Forecasting Models (GFM), have shown recently promising results in
forecasting competitions and real-world applications, outperforming many
state-of-the-art univariate forecasting techniques. In most cases, GFMs are
implemented using deep neural networks, and in particular Recurrent Neural
Networks (RNN), which require a sufficient amount of time series to estimate
their numerous model parameters. However, many time series databases have only
a limited number of time series. In this study, we propose a novel, data
augmentation based forecasting framework that is capable of improving the
baseline accuracy of the GFM models in less data-abundant settings. We use
three time series augmentation techniques: GRATIS, moving block bootstrap
(MBB), and dynamic time warping barycentric averaging (DBA) to synthetically
generate a collection of time series. The knowledge acquired from these
augmented time series is then transferred to the original dataset using two
different approaches: the pooled approach and the transfer learning approach.
When building GFMs, in the pooled approach, we train a model on the augmented
time series alongside the original time series dataset, whereas in the transfer
learning approach, we adapt a pre-trained model to the new dataset. In our
evaluation on competition and real-world time series datasets, our proposed
variants can significantly improve the baseline accuracy of GFM models and
outperform state-of-the-art univariate forecasting methods.

negative:
Time series data can be subject to changes in the underlying process that
generates them and, because of these changes, models built on old samples can
become obsolete or perform poorly. In this work, we present a way to
incorporate information about the current data distribution and its evolution
across time into machine learning algorithms. Our solution is based on
efficiently maintaining statistics, particularly the mean and the variance, of
data features at different time resolutions. These data summarisations can be
performed over the input attributes, in which case they can then be fed into
the model as additional input features, or over latent representations learned
by models, such as those of Recurrent Neural Networks. In classification tasks,
the proposed techniques can significantly outperform the prediction
capabilities of equivalent architectures with no feature / latent
summarisations. Furthermore, these modifications do not introduce notable
computational and memory overhead when properly adjusted.

negative:
We propose an approximation algorithm for efficient correlation search in
time series data. In our method, we use Fourier transform and neural network to
embed time series into a low-dimensional Euclidean space. The given space is
learned such that time series correlation can be effectively approximated from
Euclidean distance between corresponding embedded vectors. Therefore, search
for correlated time series can be done using an index in the embedding space
for efficient nearest neighbor search. Our theoretical analysis illustrates
that our method's accuracy can be guaranteed under certain regularity
conditions. We further conduct experiments on real-world datasets and the
results show that our method indeed outperforms the baseline solution. In
particular, for approximation of correlation, our method reduces the
approximation loss by a half in most test cases compared to the baseline
solution. For top-$k$ highest correlation search, our method improves the
precision from 5\% to 20\% while the query time is similar to the baseline
approach query time.

negative:
Graph analytics can lead to better quantitative understanding and control of
complex networks, but traditional methods suffer from high computational cost
and excessive memory requirements associated with the high-dimensionality and
heterogeneous characteristics of industrial size networks. Graph embedding
techniques can be effective in converting high-dimensional sparse graphs into
low-dimensional, dense and continuous vector spaces, preserving maximally the
graph structure properties. Another type of emerging graph embedding employs
Gaussian distribution-based graph embedding with important uncertainty
estimation. The main goal of graph embedding methods is to pack every node's
properties into a vector with a smaller dimension, hence, node similarity in
the original complex irregular spaces can be easily quantified in the embedded
vector spaces using standard metrics. The generated nonlinear and highly
informative graph embeddings in the latent space can be conveniently used to
address different downstream graph analytics tasks (e.g., node classification,
link prediction, community detection, visualization, etc.). In this Review, we
present some fundamental concepts in graph analytics and graph embedding
methods, focusing in particular on random walk-based and neural network-based
methods. We also discuss the emerging deep learning-based dynamic graph
embedding methods. We highlight the distinct advantages of graph embedding
methods in four diverse applications, and present implementation details and
references to open-source software as well as available databases in the
Appendix for the interested readers to start their exploration into graph
analytics.

negative:
Tasking machine learning to predict segments of a time series requires
estimating the parameters of a ML model with input/output pairs from the time
series. Using the equivalence between statistical data assimilation and
supervised machine learning, we revisit this task. The training method for the
machine utilizes a precision annealing approach to identifying the global
minimum of the action (-log[P]). In this way we are able to identify the number
of training pairs required to produce good generalizations (predictions) for
the time series. We proceed from a scalar time series $s(t_n); t_n = t_0 + n
\Delta t$ and using methods of nonlinear time series analysis show how to
produce a $D_E > 1$ dimensional time delay embedding space in which the time
series has no false neighbors as does the observed $s(t_n)$ time series. In
that $D_E$-dimensional space we explore the use of feed forward multi-layer
perceptrons as network models operating on $D_E$-dimensional input and
producing $D_E$-dimensional outputs.

negative:
High-dimensional time series are common in many domains. Since human
cognition is not optimized to work well in high-dimensional spaces, these areas
could benefit from interpretable low-dimensional representations. However, most
representation learning algorithms for time series data are difficult to
interpret. This is due to non-intuitive mappings from data features to salient
properties of the representation and non-smoothness over time. To address this
problem, we propose a new representation learning framework building on ideas
from interpretable discrete dimensionality reduction and deep generative
modeling. This framework allows us to learn discrete representations of time
series, which give rise to smooth and interpretable embeddings with superior
clustering performance. We introduce a new way to overcome the
non-differentiability in discrete representation learning and present a
gradient-based version of the traditional self-organizing map algorithm that is
more performant than the original. Furthermore, to allow for a probabilistic
interpretation of our method, we integrate a Markov model in the representation
space. This model uncovers the temporal transition structure, improves
clustering performance even further and provides additional explanatory
insights as well as a natural representation of uncertainty. We evaluate our
model in terms of clustering performance and interpretability on static
(Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST
images, a chaotic Lorenz attractor system with two macro states, as well as on
a challenging real world medical time series application on the eICU data set.
Our learned representations compare favorably with competitor methods and
facilitate downstream tasks on the real world data.

negative:
Hierarchical forecasting is a key problem in many practical multivariate
forecasting applications - the goal is to simultaneously predict a large number
of correlated time series that are arranged in a pre-specified aggregation
hierarchy. The main challenge is to exploit the hierarchical correlations to
simultaneously obtain good prediction accuracy for time series at different
levels of the hierarchy. In this paper, we propose a new approach for
hierarchical forecasting which consists of two components. First, decomposing
the time series along a global set of basis time series and modeling
hierarchical constraints using the coefficients of the basis decomposition. And
second, using a linear autoregressive model with coefficients that vary with
time. Unlike past methods, our approach is scalable (inference for a specific
time series only needs access to its own history) while also modeling the
hierarchical structure via (approximate) coherence constraints among the time
series forecasts. We experiment on several public datasets and demonstrate
significantly improved overall performance on forecasts at different levels of
the hierarchy, compared to existing state-of-the-art hierarchical models.

negative:
Time-series forecasting has been an important research domain for so many
years. Its applications include ECG predictions, sales forecasting, weather
conditions, even COVID-19 spread predictions. These applications have motivated
many researchers to figure out an optimal forecasting approach, but the
modeling approach also changes as the application domain changes. This work has
focused on reviewing different forecasting approaches for telemetry data
predictions collected at data centers. Forecasting of telemetry data is a
critical feature of network and data center management products. However, there
are multiple options of forecasting approaches that range from a simple linear
statistical model to high capacity deep learning architectures. In this paper,
we attempted to summarize and evaluate the performance of well known time
series forecasting techniques. We hope that this evaluation provides a
comprehensive summary to innovate in forecasting approaches for telemetry data.

negative:
For many machine learning tasks, the input data lie on a low-dimensional
manifold embedded in a high dimensional space and, because of this
high-dimensional structure, most algorithms are inefficient. The typical
solution is to reduce the dimension of the input data using standard dimension
reduction algorithms such as ISOMAP, LAPLACIAN EIGENMAPS or LLES. This
approach, however, does not always work in practice as these algorithms require
that we have somewhat ideal data. Unfortunately, most data sets either have
missing entries or unacceptably noisy values. That is, real data are far from
ideal and we cannot use these algorithms directly. In this paper, we focus on
the case when we have missing data. Some techniques, such as matrix completion,
can be used to fill in missing data but these methods do not capture the
non-linear structure of the manifold. Here, we present a new algorithm
MR-MISSING that extends these previous algorithms and can be used to compute
low dimensional representation on data sets with missing entries. We
demonstrate the effectiveness of our algorithm by running three different
experiments. We visually verify the effectiveness of our algorithm on synthetic
manifolds, we numerically compare our projections against those computed by
first filling in data using nlPCA and mDRUR on the MNIST data set, and we also
show that we can do classification on MNIST with missing data. We also provide
a theoretical guarantee for MR-MISSING under some simplifying assumptions.

negative:
Time-series data with missing values are commonly encountered in many fields,
such as healthcare, meteorology, and robotics. The imputation aims to fill the
missing values with valid values. Most imputation methods trained the models
implicitly because missing values have no ground truth. In this paper, we
propose Random Drop Imputation with Self-training (RDIS), a novel training
method for time-series data imputation models. In RDIS, we generate extra
missing values by applying a random drop on the observed values in incomplete
data. We can explicitly train the imputation models by filling in the randomly
dropped values. In addition, we adopt self-training with pseudo values to
exploit the original missing values. To improve the quality of pseudo values,
we set the threshold and filter them by calculating the entropy. To verify the
effectiveness of RDIS on the time series imputation, we test RDIS to various
imputation models and achieve competitive results on two real-world datasets.

negative:
The growing popularity of wearable sensors has generated large quantities of
temporal physiological and activity data. Ability to analyze this data offers
new opportunities for real-time health monitoring and forecasting. However,
temporal physiological data presents many analytic challenges: the data is
noisy, contains many missing values, and each series has a different length.
Most methods proposed for time series analysis and classification do not handle
datasets with these characteristics nor do they offer interpretability and
explainability, a critical requirement in the health domain. We propose an
unsupervised method for learning representations of time series based on common
patterns identified within them. The patterns are, interpretable, variable in
length, and extracted using Byte Pair Encoding compression technique. In this
way the method can capture both long-term and short-term dependencies present
in the data. We show that this method applies to both univariate and
multivariate time series and beats state-of-the-art approaches on a real world
dataset collected from wearable sensors.

negative:
Conditional probabilistic graphical models provide a powerful framework for
structured regression in spatio-temporal datasets with complex correlation
patterns. However, in real-life applications a large fraction of observations
is often missing, which can severely limit the representational power of these
models. In this paper we propose a Marginalized Gaussian Conditional Random
Fields (m-GCRF) structured regression model for dealing with missing labels in
partially observed temporal attributed graphs. This method is aimed at learning
with both labeled and unlabeled parts and effectively predicting future values
in a graph. The method is even capable of learning from nodes for which the
response variable is never observed in history, which poses problems for many
state-of-the-art models that can handle missing data. The proposed model is
characterized for various missingness mechanisms on 500 synthetic graphs. The
benefits of the new method are also demonstrated on a challenging application
for predicting precipitation based on partial observations of climate variables
in a temporal graph that spans the entire continental US. We also show that the
method can be useful for optimizing the costs of data collection in climate
applications via active reduction of the number of weather stations to
consider. In experiments on these real-world and synthetic datasets we show
that the proposed model is consistently more accurate than alternative
semi-supervised structured models, as well as models that either use imputation
to deal with missing values or simply ignore them altogether.

negative:
In this paper we propose a data augmentation method for time series with
irregular sampling, Time-Conditional Generative Adversarial Network (T-CGAN).
Our approach is based on Conditional Generative Adversarial Networks (CGAN),
where the generative step is implemented by a deconvolutional NN and the
discriminative step by a convolutional NN. Both the generator and the
discriminator are conditioned on the sampling timestamps, to learn the hidden
relationship between data and timestamps, and consequently to generate new time
series. We evaluate our model with synthetic and real-world datasets. For the
synthetic data, we compare the performance of a classifier trained with
T-CGAN-generated data, against the performance of the same classifier trained
on the original data. Results show that classifiers trained on T-CGAN-generated
data perform the same as classifiers trained on real data, even with very short
time series and small training sets. For the real world datasets, we compare
our method with other techniques of data augmentation for time series, such as
time slicing and time warping, over a classification problem with unbalanced
datasets. Results show that our method always outperforms the other approaches,
both in case of regularly sampled and irregularly sampled time series. We
achieve particularly good performance in case with a small training set and
short, noisy, irregularly-sampled time series.

negative:
Time series forecasting is a growing domain with diverse applications.
However, changes of the system behavior over time due to internal or external
influences are challenging. Therefore, predictions of a previously learned
fore-casting model might not be useful anymore. In this paper, we present
EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal
Data (EVARS-GPR), a novel online algorithm that is able to handle sudden shifts
in the target variable scale of seasonal data. For this purpose, EVARS-GPR
com-bines online change point detection with a refitting of the prediction
model using data augmentation for samples prior to a change point. Our
experiments on sim-ulated data show that EVARS-GPR is applicable for a wide
range of output scale changes. EVARS-GPR has on average a 20.8 % lower RMSE on
different real-world datasets compared to methods with a similar computational
resource con-sumption. Furthermore, we show that our algorithm leads to a
six-fold reduction of the averaged runtime in relation to all comparison
partners with a periodical refitting strategy. In summary, we present a
computationally efficient online fore-casting algorithm for seasonal time
series with changes of the target variable scale and demonstrate its
functionality on simulated as well as real-world data. All code is publicly
available on GitHub: https://github.com/grimmlab/evars-gpr.

negative:
High-dimensional probability density estimation for inference suffers from
the "curse of dimensionality". For many physical inference problems, the full
posterior distribution is unwieldy and seldom used in practice. Instead, we
propose direct estimation of lower-dimensional marginal distributions,
bypassing high-dimensional density estimation or high-dimensional Markov chain
Monte Carlo (MCMC) sampling. By evaluating the two-dimensional marginal
posteriors we can unveil the full-dimensional parameter covariance structure.
We additionally propose constructing a simple hierarchy of fast neural
regression models, called Moment Networks, that compute increasing moments of
any desired lower-dimensional marginal posterior density; these reproduce exact
results from analytic posteriors and those obtained from Masked Autoregressive
Flows. We demonstrate marginal posterior density estimation using
high-dimensional LIGO-like gravitational wave time series and describe
applications for problems of fundamental cosmology.

negative:
Despite the popularisation of machine learning models, more often than not,
they still operate as black boxes with no insight into what is happening inside
the model. There exist a few methods that allow to visualise and explain why a
model has made a certain prediction. Those methods, however, allow
visualisation of the link between the input and output of the model without
presenting how the model learns to represent the data used to train the model
as whole. In this paper, a method that addresses that issue is proposed, with a
focus on visualising multi-dimensional time-series data. Experiments on a
high-frequency stock market dataset show that the method provides fast and
discernible visualisations. Large datasets can be visualised quickly and on one
plot, which makes it easy for a user to compare the learned representations of
the data. The developed method successfully combines known techniques to
provide an insight into the inner workings of time-series classification
models.

negative:
Temporal anomaly detection looks for irregularities over space-time.
Unsupervised temporal models employed thus far typically work on sequences of
feature vectors, and much less on temporal multiway data. We focus our
investigation on two-way data, in which a data matrix is observed at each time
step. Leveraging recent advances in matrix-native recurrent neural networks, we
investigated strategies for data arrangement and unsupervised training for
temporal multiway anomaly detection. These include compressing-decompressing,
encoding-predicting, and temporal data differencing. We conducted a
comprehensive suite of experiments to evaluate model behaviors under various
settings on synthetic data, moving digits, and ECG recordings. We found
interesting phenomena not previously reported. These include the capacity of
the compact matrix LSTM to compress noisy data near perfectly, making the
strategy of compressing-decompressing data ill-suited for anomaly detection
under the noise. Also, long sequence of vectors can be addressed directly by
matrix models that allow very long context and multiple step prediction.
Overall, the encoding-predicting strategy works very well for the matrix LSTMs
in the conducted experiments, thanks to its compactness and better fit to the
data dynamics.

negative:
Sparse regression such as the Lasso has achieved great success in handling
high-dimensional data. However, one of the biggest practical problems is that
high-dimensional data often contain large amounts of missing values. Convex
Conditioned Lasso (CoCoLasso) has been proposed for dealing with
high-dimensional data with missing values, but it performs poorly when there
are many missing values, so that the high missing rate problem has not been
resolved. In this paper, we propose a novel Lasso-type regression method for
high-dimensional data with high missing rates. We effectively incorporate mean
imputed covariance, overcoming its inherent estimation bias. The result is an
optimally weighted modification of CoCoLasso according to missing ratios. We
theoretically and experimentally show that our proposed method is highly
effective even when there are many missing values.

negative:
A cornerstone of the worldwide transition to smart grids are smart meters.
Smart meters typically collect and provide energy time series that are vital
for various applications, such as grid simulations, fault-detection, load
forecasting, load analysis, and load management. Unfortunately, these time
series are often characterized by missing values that must be handled before
the data can be used. A common approach to handle missing values in time series
is imputation. However, existing imputation methods are designed for power time
series and do not take into account the total energy of gaps, resulting in
jumps or constant shifts when imputing energy time series. In order to overcome
these issues, the present paper introduces the new Copy-Paste Imputation (CPI)
method for energy time series. The CPI method copies data blocks with similar
properties and pastes them into gaps of the time series while preserving the
total energy of each gap. The new method is evaluated on a real-world dataset
that contains six shares of artificially inserted missing values between 1 and
30%. It outperforms by far the three benchmark imputation methods selected for
comparison. The comparison furthermore shows that the CPI method uses matching
patterns and preserves the total energy of each gap while requiring only a
moderate run-time.

negative:
In the context of time series forecasting, it is a common practice to
evaluate multiple methods and choose one of these methods or an ensemble for
producing the best forecasts. However, choosing among different ensembles over
multiple methods remains a challenging task that undergoes a combinatorial
explosion as the number of methods increases. In the context of demand
forecasting or revenue forecasting, this challenge is further exacerbated by a
large number of time series as well as limited historical data points available
due to changing business context. Although deep learning forecasting methods
aim to simultaneously forecast large collections of time series, they become
challenging to apply in such scenarios due to the limited history available and
might not yield desirable results. We propose a framework for forecasting short
high-dimensional time series data by combining low-rank temporal matrix
factorization and optimal model selection on latent time series using
cross-validation. We demonstrate that forecasting the latent factors leads to
significant performance gains as compared to directly applying different
uni-variate models on time series. Performance has been validated on a
truncated version of the M4 monthly dataset which contains time series data
from multiple domains showing the general applicability of the method.
Moreover, it is amenable to incorporating the analyst view of the future owing
to the low number of latent factors which is usually impractical when applying
forecasting methods directly to high dimensional datasets.

negative:
Accurate demand forecasts can help on-line retail organizations better plan
their supply-chain processes. The challenge, however, is the large number of
associative factors that result in large, non-stationary shifts in demand,
which traditional time series and regression approaches fail to model. In this
paper, we propose a Neural Network architecture called AR-MDN, that
simultaneously models associative factors, time-series trends and the variance
in the demand. We first identify several causal features and use a combination
of feature embeddings, MLP and LSTM to represent them. We then model the output
density as a learned mixture of Gaussian distributions. The AR-MDN can be
trained end-to-end without the need for additional supervision. We experiment
on a dataset of an year's worth of data over tens-of-thousands of products from
Flipkart. The proposed architecture yields a significant improvement in
forecasting accuracy when compared with existing alternatives.

negative:
This paper presents a time series forecasting framework which combines
standard forecasting methods and a machine learning model. The inputs to the
machine learning model are not lagged values or regular time series features,
but instead forecasts produced by standard methods. The machine learning model
can be either a convolutional neural network model or a recurrent neural
network model. The intuition behind this approach is that forecasts of a time
series are themselves good features characterizing the series, especially when
the modelling purpose is forecasting. It can also be viewed as a weighted
ensemble method. Tested on the M4 competition dataset, this approach
outperforms all submissions for quarterly series, and is more accurate than all
but the winning algorithm for monthly series.

negative:
Conditional modeling x \to y is a central problem in machine learning. A
substantial research effort is devoted to such modeling when x is high
dimensional. We consider, instead, the case of a high dimensional y, where x is
either low dimensional or high dimensional. Our approach is based on selecting
a small subset y_L of the dimensions of y, and proceed by modeling (i) x \to
y_L and (ii) y_L \to y. Composing these two models, we obtain a conditional
model x \to y that possesses convenient statistical properties. Multi-label
classification and multivariate regression experiments on several datasets show
that this model outperforms the one vs. all approach as well as several
sophisticated multiple output prediction methods.

negative:
A networked time series (NETS) is a family of time series on a given graph,
one for each node. It has found a wide range of applications from intelligent
transportation, environment monitoring to mobile network management. An
important task in such applications is to predict the future values of a NETS
based on its historical values and the underlying graph. Most existing methods
require complete data for training. However, in real-world scenarios, it is not
uncommon to have missing data due to sensor malfunction, incomplete sensing
coverage, etc. In this paper, we study the problem of NETS prediction with
incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that
can be trained on incomplete data with missing values in both history and
future. Furthermore, we propose novel Graph Temporal Attention Networks by
incorporating the attention mechanism to capture both inter-time series
correlations and temporal correlations. We conduct extensive experiments on
three real-world datasets under different missing patterns and missing rates.
The experimental results show that NETS-ImpGAN outperforms existing methods
except when data exhibit very low variance, in which case NETS-ImpGAN still
achieves competitive performance.

negative:
Finding rare information hidden in a huge amount of data from the Internet is
a necessary but complex issue. Many researchers have studied this issue and
have found effective methods to detect anomaly data in low dimensional space.
However, as the dimension increases, most of these existing methods perform
poorly in detecting outliers because of "high dimensional curse". Even though
some approaches aim to solve this problem in high dimensional space, they can
only detect some anomaly data appearing in low dimensional space and cannot
detect all of anomaly data which appear differently in high dimensional space.
To cope with this problem, we propose a new k-nearest section-based method
(k-NS) in a section-based space. Our proposed approach not only detects
outliers in low dimensional space with section-density ratio but also detects
outliers in high dimensional space with the ratio of k-nearest section against
average value. After taking a series of experiments with the dimension from 10
to 10000, the experiment results show that our proposed method achieves 100%
precision and 100% recall result in the case of extremely high dimensional
space, and better improvement in low dimensional space compared to our
previously proposed method.

negative:
We present the collaborative Kalman filter (CKF), a dynamic model for
collaborative filtering and related factorization models. Using the matrix
factorization approach to collaborative filtering, the CKF accounts for time
evolution by modeling each low-dimensional latent embedding as a
multidimensional Brownian motion. Each observation is a random variable whose
distribution is parameterized by the dot product of the relevant Brownian
motions at that moment in time. This is naturally interpreted as a Kalman
filter with multiple interacting state space vectors. We also present a method
for learning a dynamically evolving drift parameter for each location by
modeling it as a geometric Brownian motion. We handle posterior intractability
via a mean-field variational approximation, which also preserves tractability
for downstream calculations in a manner similar to the Kalman filter. We
evaluate the model on several large datasets, providing quantitative evaluation
on the 10 million Movielens and 100 million Netflix datasets and qualitative
evaluation on a set of 39 million stock returns divided across roughly 6,500
companies from the years 1962-2014.

negative:
We present a representation learning framework for financial time series
forecasting. One challenge of using deep learning models for finance
forecasting is the shortage of available training data when using small
datasets. Direct trend classification using deep neural networks trained on
small datasets is susceptible to the overfitting problem. In this paper, we
propose to first learn compact representations from time series data, then use
the learned representations to train a simpler model for predicting time series
movements. We consider a class-conditioned latent variable model. We train an
encoder network to maximize the mutual information between the latent variables
and the trend information conditioned on the encoded observed variables. We
show that conditional mutual information maximization can be approximated by a
contrastive loss. Then, the problem is transformed into a classification task
of determining whether two encoded representations are sampled from the same
class or not. This is equivalent to performing pairwise comparisons of the
training datapoints, and thus, improves the generalization ability of the
encoder network. We use deep autoregressive models as our encoder to capture
long-term dependencies of the sequence data. Empirical experiments indicate
that our proposed method has the potential to advance state-of-the-art
performance.

negative:
In this paper, we propose a machine learning approach for forecasting
hierarchical time series. When dealing with hierarchical time series, apart
from generating accurate forecasts, one needs to select a suitable method for
producing reconciled forecasts. Forecast reconciliation is the process of
adjusting forecasts to make them coherent across the hierarchy. In literature,
coherence is often enforced by using a post-processing technique on the base
forecasts produced by suitable time series forecasting methods. On the
contrary, our idea is to use a deep neural network to directly produce accurate
and reconciled forecasts. We exploit the ability of a deep neural network to
extract information capturing the structure of the hierarchy. We impose the
reconciliation at training time by minimizing a customized loss function. In
many practical applications, besides time series data, hierarchical time series
include explanatory variables that are beneficial for increasing the
forecasting accuracy. Exploiting this further information, our approach links
the relationship between time series features extracted at any level of the
hierarchy and the explanatory variables into an end-to-end neural network
providing accurate and reconciled point forecasts. The effectiveness of the
approach is validated on three real-world datasets, where our method
outperforms state-of-the-art competitors in hierarchical forecasting.

negative:
Time series analysis is a field of data science which is interested in
analyzing sequences of numerical values ordered in time. Time series are
particularly interesting because they allow us to visualize and understand the
evolution of a process over time. Their analysis can reveal trends,
relationships and similarities across the data. There exists numerous fields
containing data in the form of time series: health care (electrocardiogram,
blood sugar, etc.), activity recognition, remote sensing, finance (stock market
price), industry (sensors), etc. Time series classification consists of
constructing algorithms dedicated to automatically label time series data. The
sequential aspect of time series data requires the development of algorithms
that are able to harness this temporal property, thus making the existing
off-the-shelf machine learning models for traditional tabular data suboptimal
for solving the underlying task. In this context, deep learning has emerged in
recent years as one of the most effective methods for tackling the supervised
classification task, particularly in the field of computer vision. The main
objective of this thesis was to study and develop deep neural networks
specifically constructed for the classification of time series data. We thus
carried out the first large scale experimental study allowing us to compare the
existing deep methods and to position them compared other non-deep learning
based state-of-the-art methods. Subsequently, we made numerous contributions in
this area, notably in the context of transfer learning, data augmentation,
ensembling and adversarial attacks. Finally, we have also proposed a novel
architecture, based on the famous Inception network (Google), which ranks among
the most efficient to date.

negative:
Missing value imputation is crucial for real-world data science workflows.
Imputation is harder in the online setting, as it requires the imputation
method itself to be able to evolve over time. For practical applications,
imputation algorithms should produce imputations that match the true data
distribution, handle data of mixed types, including ordinal, boolean, and
continuous variables, and scale to large datasets. In this work we develop a
new online imputation algorithm for mixed data using the Gaussian copula. The
online Gaussian copula model meets all the desiderata: its imputations match
the data distribution even for mixed data, improve over its offline counterpart
on the accuracy when the streaming data has a changing distribution, and on the
speed (up to an order of magnitude) especially on large scale datasets. By
fitting the copula model to online data, we also provide a new method to detect
change points in the multivariate dependence structure with missing values.
Experimental results on synthetic and real world data validate the performance
of the proposed methods.

negative:
Time series prediction with missing values is an important problem of time
series analysis since complete data is usually hard to obtain in many
real-world applications. To model the generation of time series, autoregressive
(AR) model is a basic and widely used one, which assumes that each observation
in the time series is a noisy linear combination of some previous observations
along with a constant shift. To tackle the problem of prediction with missing
values, a number of methods were proposed based on various data models. For
real application scenarios, how do these methods perform over different types
of time series with different levels of data missing remains to be
investigated. In this paper, we focus on online methods for AR-model-based time
series prediction with missing values. We adapted five mainstream methods to
fit in such a scenario. We make detailed discussion on each of them by
introducing their core ideas about how to estimate the AR coefficients and
their different strategies to deal with missing values. We also present
algorithmic implementations for better understanding. In order to
comprehensively evaluate these methods and do the comparison, we conduct
experiments with various configurations of relative parameters over both
synthetic and real data. From the experimental results, we derived several
noteworthy conclusions and shows that imputation is a simple but reliable
strategy to handle missing values in online prediction tasks.

negative:
Visualizing high dimensional data by projecting them into two or three
dimensional space is one of the most effective ways to intuitively understand
the data's underlying characteristics, for example their class neighborhood
structure. While data visualization in low dimensional space can be efficient
for revealing the data's underlying characteristics, classifying a new sample
in the reduced-dimensional space is not always beneficial because of the loss
of information in expressing the data. It is possible to classify the data in
the high dimensional space, while visualizing them in the low dimensional
space, but in this case, the visualization is often meaningless because it
fails to illustrate the underlying characteristics that are crucial for the
classification process.
  In this paper, the performance-preserving property of the previously proposed
Restricted Radial Basis Function Network in reducing the dimension of labeled
data is explained. Here, it is argued through empirical experiments that the
internal representation of the Restricted Radial Basis Function Network, which
during the supervised learning process organizes a visualizable two dimensional
map, does not only preserve the topographical structure of high dimensional
data but also captures their class neighborhood structures that are important
for classifying them. Hence, unlike many of the existing dimension reduction
methods, the Restricted Radial Basis Function Network offers two dimensional
visualization that is strongly correlated with the classification process.

negative:
We present an approach to analyze $C^1(\mathbb{R}^m)$ functions that
addresses limitations present in the Active Subspaces (AS) method of
Constantine et al.(2015; 2014). Under appropriate hypotheses, our Active
Manifolds (AM) method identifies a 1-D curve in the domain (the active
manifold) on which nearly all values of the unknown function are attained, and
which can be exploited for approximation or analysis, especially when $m$ is
large (high-dimensional input space). We provide theorems justifying our AM
technique and an algorithm permitting functional approximation and sensitivity
analysis. Using accessible, low-dimensional functions as initial examples, we
show AM reduces approximation error by an order of magnitude compared to AS, at
the expense of more computation. Following this, we revisit the sensitivity
analysis by Glaws et al. (2017), who apply AS to analyze a magnetohydrodynamic
power generator model, and compare the performance of AM on the same data. Our
analysis provides detailed information not captured by AS, exhibiting the
influence of each parameter individually along an active manifold. Overall, AM
represents a novel technique for analyzing functional models with benefits
including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more
accurate regression than AS (at more computational expense), enabling more
informative sensitivity analysis, and granting accessible visualizations(2-D
plots) of parameter sensitivity along the AM.

negative:
Accurate time-series forecasting is vital for numerous areas of application
such as transportation, energy, finance, economics, etc. However, while modern
techniques are able to explore large sets of temporal data to build forecasting
models, they typically neglect valuable information that is often available
under the form of unstructured text. Although this data is in a radically
different format, it often contains contextual explanations for many of the
patterns that are observed in the temporal data. In this paper, we propose two
deep learning architectures that leverage word embeddings, convolutional layers
and attention mechanisms for combining text information with time-series data.
We apply these approaches for the problem of taxi demand forecasting in event
areas. Using publicly available taxi data from New York, we empirically show
that by fusing these two complementary cross-modal sources of information, the
proposed models are able to significantly reduce the error in the forecasts.

negative:
Time-series classification is one of the most frequently performed tasks in
industrial data science, and one of the most widely used data representation in
the industrial setting is tabular representation. In this work, we propose a
novel scalable architecture for learning representations from tabular
time-series data and subsequently performing downstream tasks such as
time-series classification. The representation learning framework is
end-to-end, akin to bidirectional encoder representations from transformers
(BERT) in language modeling, however, we introduce novel masking technique
suitable for pretraining of time-series data. Additionally, we also use
one-dimensional convolutions augmented with transformers and explore their
effectiveness, since the time-series datasets lend themselves naturally for
one-dimensional convolutions. We also propose a novel timestamp embedding
technique, which helps in handling both periodic cycles at different time
granularity levels, and aperiodic trends present in the time-series data. Our
proposed model is end-to-end and can handle both categorical and continuous
valued inputs, and does not require any quantization or encoding of continuous
features.

negative:
Interactive response time is important in analytical pipelines for users to
explore a sufficient number of possibilities and make informed business
decisions. We consider a forecasting pipeline with large volumes of
high-dimensional time series data. Real-time forecasting can be conducted in
two steps. First, we specify the part of data to be focused on and the measure
to be predicted by slicing, dicing, and aggregating the data. Second, a
forecasting model is trained on the aggregated results to predict the trend of
the specified measure. While there are a number of forecasting models
available, the first step is the performance bottleneck. A natural idea is to
utilize sampling to obtain approximate aggregations in real time as the input
to train the forecasting model. Our scalable real-time forecasting system
FlashP (Flash Prediction) is built based on this idea, with two major
challenges to be resolved in this paper: first, we need to figure out how
approximate aggregations affect the fitting of forecasting models, and
forecasting results; and second, accordingly, what sampling algorithms we
should use to obtain these approximate aggregations and how large the samples
are. We introduce a new sampling scheme, called GSW sampling, and analyze error
bounds for estimating aggregations using GSW samples. We introduce how to
construct compact GSW samples with the existence of multiple measures to be
analyzed. We conduct experiments to evaluate our solution and compare it with
alternatives on real data.

negative:
In this paper, we present a new approach to time series forecasting. Time
series data are prevalent in many scientific and engineering disciplines. Time
series forecasting is a crucial task in modeling time series data, and is an
important area of machine learning. In this work we developed a novel method
that employs Transformer-based machine learning models to forecast time series
data. This approach works by leveraging self-attention mechanisms to learn
complex patterns and dynamics from time series data. Moreover, it is a generic
framework and can be applied to univariate and multivariate time series data,
as well as time series embeddings. Using influenza-like illness (ILI)
forecasting as a case study, we show that the forecasting results produced by
our approach are favorably comparable to the state-of-the-art.

negative:
Kernel density estimation is a simple and effective method that lies at the
heart of many important machine learning applications. Unfortunately, kernel
methods scale poorly for large, high dimensional datasets. Approximate kernel
density estimation has a prohibitively high memory and computation cost,
especially in the streaming setting. Recent sampling algorithms for high
dimensional densities can reduce the computation cost but cannot operate
online, while streaming algorithms cannot handle high dimensional datasets due
to the curse of dimensionality. We propose RACE, an efficient sketching
algorithm for kernel density estimation on high-dimensional streaming data.
RACE compresses a set of N high dimensional vectors into a small array of
integer counters. This array is sufficient to estimate the kernel density for a
large class of kernels. Our sketch is practical to implement and comes with
strong theoretical guarantees. We evaluate our method on real-world
high-dimensional datasets and show that our sketch achieves 10x better
compression compared to competing methods.

negative:
Time series forecasting is essential for agents to make decisions.
Traditional approaches rely on statistical methods to forecast given past
numeric values. In practice, end-users often rely on visualizations such as
charts and plots to reason about their forecasts. Inspired by practitioners, we
re-imagine the topic by creating a novel framework to produce visual forecasts,
similar to the way humans intuitively do. In this work, we leverage advances in
deep learning to extend the field of time series forecasting to a visual
setting. We capture input data as an image and train a model to produce the
subsequent image. This approach results in predicting distributions as opposed
to pointwise values. We examine various synthetic and real datasets with
diverse degrees of complexity. Our experiments show that visual forecasting is
effective for cyclic data but somewhat less for irregular data such as stock
price. Importantly, when using image-based evaluation metrics, we find the
proposed visual forecasting method to outperform various numerical baselines,
including ARIMA and a numerical variation of our method. We demonstrate the
benefits of incorporating vision-based approaches in forecasting tasks -- both
for the quality of the forecasts produced, as well as the metrics that can be
used to evaluate them.

negative:
Multivariate time-series data are frequently observed in critical care
settings and are typically characterized by sparsity (missing information) and
irregular time intervals. Existing approaches for learning representations in
this domain handle these challenges by either aggregation or imputation of
values, which in-turn suppresses the fine-grained information and adds
undesirable noise/overhead into the machine learning model. To tackle this
problem, we propose a Self-supervised Transformer for Time-Series (STraTS)
model which overcomes these pitfalls by treating time-series as a set of
observation triplets instead of using the standard dense matrix representation.
It employs a novel Continuous Value Embedding technique to encode continuous
time and variable values without the need for discretization. It is composed of
a Transformer component with multi-head attention layers which enable it to
learn contextual triplet embeddings while avoiding the problems of recurrence
and vanishing gradients that occur in recurrent architectures. In addition, to
tackle the problem of limited availability of labeled data (which is typically
observed in many healthcare applications), STraTS utilizes self-supervision by
leveraging unlabeled data to learn better representations by using time-series
forecasting as an auxiliary proxy task. Experiments on real-world multivariate
clinical time-series benchmark datasets demonstrate that STraTS has better
prediction performance than state-of-the-art methods for mortality prediction,
especially when labeled data is limited. Finally, we also present an
interpretable version of STraTS which can identify important measurements in
the time-series data. Our data preprocessing and model implementation codes are
available at https://github.com/sindhura97/STraTS.

negative:
This paper describes a novel approach to change-point detection when the
observed high-dimensional data may have missing elements. The performance of
classical methods for change-point detection typically scales poorly with the
dimensionality of the data, so that a large number of observations are
collected after the true change-point before it can be reliably detected.
Furthermore, missing components in the observed data handicap conventional
approaches. The proposed method addresses these challenges by modeling the
dynamic distribution underlying the data as lying close to a time-varying
low-dimensional submanifold embedded within the ambient observation space.
Specifically, streaming data is used to track a submanifold approximation,
measure deviations from this approximation, and calculate a series of
statistics of the deviations for detecting when the underlying manifold has
changed in a sharp or unexpected manner. The approach described in this paper
leverages several recent results in the field of high-dimensional data
analysis, including subspace tracking with missing data, multiscale analysis
techniques for point clouds, online optimization, and change-point detection
performance analysis. Simulations and experiments highlight the robustness and
efficacy of the proposed approach in detecting an abrupt change in an otherwise
slowly varying low-dimensional manifold.

negative:
We extract and use player position time-series data, tagged along with the
action types, to build a competent model for representing team tactics
behavioral patterns and use this representation to predict the outcome of
arbitrary movements. We provide a framework for the useful encoding of short
tactics and space occupations in a more extended sequence of movements or
tactical plans. We investigate game segments during a match in which the team
in possession of the ball regularly attempts to reach a position where they can
take a shot at goal for a single game. A carefully designed and efficient
kernel is employed using a triangular fuzzy membership function to create
multiple time series for players' potential of presence at different court
regions. Unsupervised learning is then used for time series using triplet loss
and deep neural networks with exponentially dilated causal convolutions for the
derived multivariate time series. This works key contribution lies in its
approach to model how short scenes contribute to other longer ones and how
players occupies and creates new spaces in-game court. We discuss the
effectiveness of the proposed approach for prediction and recognition tasks on
the professional basketball SportVU dataset for the 2015-16 half-season. The
proposed system demonstrates descent functionality even with relatively small
data.

negative:
Many businesses and industries nowadays rely on large quantities of time
series data making time series forecasting an important research area. Global
forecasting models that are trained across sets of time series have shown a
huge potential in providing accurate forecasts compared with the traditional
univariate forecasting models that work on isolated series. However, there are
currently no comprehensive time series archives for forecasting that contain
datasets of time series from similar sources available for the research
community to evaluate the performance of new global forecasting algorithms over
a wide variety of datasets. In this paper, we present such a comprehensive time
series forecasting archive containing 20 publicly available time series
datasets from varied domains, with different characteristics in terms of
frequency, series lengths, and inclusion of missing values. We also
characterise the datasets, and identify similarities and differences among
them, by conducting a feature analysis. Furthermore, we present the performance
of a set of standard baseline forecasting methods over all datasets across
eight error metrics, for the benefit of researchers using the archive to
benchmark their forecasting algorithms.

negative:
This paper presents a machine learning approach to model the electric
consumption of electric vehicles at macroscopic level, i.e., in the absence of
a speed profile, while preserving microscopic level accuracy. For this work, we
leveraged a high-performance, agent-based transportation tool to model trips
that occur in the Greater Chicago region under various scenario changes, along
with physics-based modeling and simulation tools to provide high-fidelity
energy consumption values. The generated results constitute a very large
dataset of vehicle-route energy outcomes that capture variability in vehicle
and routing setting, and in which high-fidelity time series of vehicle speed
dynamics is masked. We show that although all internal dynamics that affect
energy consumption are masked, it is possible to learn aggregate-level energy
consumption values quite accurately with a deep learning approach. When
large-scale data is available, and with carefully tailored feature engineering,
a well-designed model can overcome and retrieve latent information. This model
has been deployed and integrated within POLARIS Transportation System
Simulation Tool to support real-time behavioral transportation models for
individual charging decision-making, and rerouting of electric vehicles.

negative:
This work contributes to the development of neural forecasting models with
novel randomization-based learning methods. These methods improve the fitting
abilities of the neural model, in comparison to the standard method, by
generating network parameters in accordance with the data and target function
features. A pattern-based representation of time series makes the proposed
approach useful for forecasting time series with multiple seasonality. In the
simulation study, we evaluate the performance of the proposed models and find
that they can compete in terms of forecasting accuracy with fully-trained
networks. Extremely fast and easy training, simple architecture, ease of
implementation, high accuracy as well as dealing with nonstationarity and
multiple seasonality in time series make the proposed model very attractive for
a wide range of complex time series forecasting problems.

negative:
This paper addresses the problem of multi-step time series forecasting for
non-stationary signals that can present sudden changes. Current
state-of-the-art deep learning forecasting methods, often trained with variants
of the MSE, lack the ability to provide sharp predictions in deterministic and
probabilistic contexts. To handle these challenges, we propose to incorporate
shape and temporal criteria in the training objective of deep models. We define
shape and temporal similarities and dissimilarities, based on a smooth
relaxation of Dynamic Time Warping (DTW) and Temporal Distortion Index (TDI),
that enable to build differentiable loss functions and positive semi-definite
(PSD) kernels. With these tools, we introduce DILATE (DIstortion Loss including
shApe and TimE), a new objective for deterministic forecasting, that explicitly
incorporates two terms supporting precise shape and temporal change detection.
For probabilistic forecasting, we introduce STRIPE++ (Shape and Time diverRsIty
in Probabilistic forEcasting), a framework for providing a set of sharp and
diverse forecasts, where the structured shape and time diversity is enforced
with a determinantal point process (DPP) diversity loss. Extensive experiments
and ablations studies on synthetic and real-world datasets confirm the benefits
of leveraging shape and time features in time series forecasting.

negative:
Decomposing complex time series into trend, seasonality, and remainder
components is an important task to facilitate time series anomaly detection and
forecasting. Although numerous methods have been proposed, there are still many
time series characteristics exhibiting in real-world data which are not
addressed properly, including 1) ability to handle seasonality fluctuation and
shift, and abrupt change in trend and reminder; 2) robustness on data with
anomalies; 3) applicability on time series with long seasonality period. In the
paper, we propose a novel and generic time series decomposition algorithm to
address these challenges. Specifically, we extract the trend component robustly
by solving a regression problem using the least absolute deviations loss with
sparse regularization. Based on the extracted trend, we apply the the non-local
seasonal filtering to extract the seasonality component. This process is
repeated until accurate decomposition is obtained. Experiments on different
synthetic and real-world time series datasets demonstrate that our method
outperforms existing solutions.

negative:
Additive Manufacturing presents a great application area for Machine Learning
because of the vast volume of data generated and the potential to mine this
data to control outcomes. In this paper we present preliminary work on
classifying infrared time-series data representing melt-pool temperature in a
metal 3D printing process. Our ultimate objective is to use this data to
predict process outcomes (e.g. hardness, porosity, surface roughness). In the
work presented here we simply show that there is a signal in this data that can
be used for the classification of different components and stages of the AM
process. In line with other Machine Learning research on time-series
classification we use k-Nearest Neighbour classifiers. The results we present
suggests that Dynamic Time Warping is an effective distance measure compared
with alternatives for 3D printing data of this type.

negative:
While artificial intelligence (AI)-based decision-making systems are
increasingly popular, significant concerns on the potential discrimination
during the AI decision-making process have been observed. For example, the
distribution of predictions is usually biased and dependents on the sensitive
attributes (e.g., gender and ethnicity). Numerous approaches have therefore
been proposed to develop decision-making systems that are
discrimination-conscious by-design, which are typically batch-based and require
the simultaneous availability of all the training data for model learning.
However, in the real-world, the data streams usually come on the fly which
requires the model to process each input data once "on arrival" and without the
need for storage and reprocessing. In addition, the data streams might also
evolve over time, which further requires the model to be able to simultaneously
adapt to non-stationary data distributions and time-evolving bias patterns,
with an effective and robust trade-off between accuracy and fairness. In this
paper, we propose a novel framework of online decision tree with fairness in
the data stream with possible distribution drifting. Specifically, first, we
propose two novel fairness splitting criteria that encode the data as well as
possible, while simultaneously removing dependence on the sensitive attributes,
and further adapts to non-stationary distribution with fine-grained control
when needed. Second, we propose two fairness decision tree online growth
algorithms that fulfills different online fair decision-making requirements.
Our experiments show that our algorithms are able to deal with discrimination
in massive and non-stationary streaming environments, with a better trade-off
between fairness and predictive performance.

negative:
Time series are widely used as signals in many classification/regression
tasks. It is ubiquitous that time series contains many missing values. Given
multiple correlated time series data, how to fill in missing values and to
predict their class labels? Existing imputation methods often impose strong
assumptions of the underlying data generating process, such as linear dynamics
in the state space. In this paper, we propose BRITS, a novel method based on
recurrent neural networks for missing value imputation in time series data. Our
proposed method directly learns the missing values in a bidirectional recurrent
dynamical system, without any specific assumption. The imputed values are
treated as variables of RNN graph and can be effectively updated during the
backpropagation.BRITS has three advantages: (a) it can handle multiple
correlated missing values in time series; (b) it generalizes to time series
with nonlinear dynamics underlying; (c) it provides a data-driven imputation
procedure and applies to general settings with missing data.We evaluate our
model on three real-world datasets, including an air quality dataset, a
health-care data, and a localization data for human activity. Experiments show
that our model outperforms the state-of-the-art methods in both imputation and
classification/regression accuracies.

negative:
Time-Spatial data plays a crucial role for different fields such as traffic
management. These data can be collected via devices such as surveillance
sensors or tracking systems. However, how to efficiently an- alyze and
visualize these data to capture essential embedded pattern information is
becoming a big challenge today. Classic visualization ap- proaches focus on
revealing 2D and 3D spatial information and modeling statistical test. Those
methods would easily fail when data become mas- sive. Recent attempts concern
on how to simply cluster data and perform prediction with time-oriented
information. However, those approaches could still be further enhanced as they
also have limitations for han- dling massive clusters and labels. In this
paper, we propose a visualiza- tion methodology for mobility data using
artificial neural net techniques. This method aggregates three main parts that
are Back-end Data Model, Neural Net Algorithm including clustering method
Self-Organizing Map (SOM) and prediction approach Recurrent Neural Net (RNN)
for ex- tracting the features and lastly a solid front-end that displays the
results to users with an interactive system. SOM is able to cluster the
visiting patterns and detect the abnormal pattern. RNN can perform the predic-
tion for time series analysis using its dynamic architecture. Furthermore, an
interactive system will enable user to interpret the result with graph- ics,
animation and 3D model for a close-loop feedback. This method can be
particularly applied in two tasks that Commercial-based Promotion and abnormal
traffic patterns detection.

negative:
A key obstacle in automated analytics and meta-learning is the inability to
recognize when different datasets contain measurements of the same variable.
Because provided attribute labels are often uninformative in practice, this
task may be more robustly addressed by leveraging the data values themselves
rather than just relying on their arbitrarily selected variable names. Here, we
present a computationally efficient method to identify high-confidence variable
matches between a given set of data values and a large repository of previously
encountered datasets. Our approach enjoys numerous advantages over
distributional similarity based techniques because we leverage learned vector
embeddings of datasets which adaptively account for natural forms of data
variation encountered in practice. Based on the neural architecture of deep
sets, our embeddings can be computed for both numeric and string data. In
dataset search and schema matching tasks, our methods outperform standard
statistical techniques and we find that the learned embeddings generalize well
to new data sources.

negative:
Embeddings are one of the fundamental building blocks for data analysis
tasks. Embeddings are already essential tools for large language models and
image analysis, and their use is being extended to many other research domains.
The generation of these distributed representations is often a data- and
computation-expensive process; yet the holistic analysis and adjustment of them
after they have been created is still a developing area. In this paper, we
first propose a very general quantitatively measure for the presence of
features in the embedding data based on if it can be learned. We then devise a
method to remove or alleviate undesired features in the embedding while
retaining the essential structure of the data. We use a Domain Adversarial
Network (DAN) to generate a non-affine transformation, but we add constraints
to ensure the essential structure of the embedding is preserved. Our empirical
results demonstrate that the proposed algorithm significantly outperforms the
state-of-art unsupervised algorithm on several data sets, including novel
applications from the industry.

negative:
Time series are series of values ordered by time. This kind of data can be
found in many real world settings. Classifying time series is a difficult task
and an active area of research. This paper investigates the use of transfer
learning in Deep Neural Networks and a 2D representation of time series known
as Recurrence Plots. In order to utilize the research done in the area of image
classification, where Deep Neural Networks have achieved very good results, we
use a Residual Neural Networks architecture known as ResNet. As preprocessing
of time series is a major part of every time series classification pipeline,
the method proposed simplifies this step and requires only few parameters. For
the first time we propose a method for multi time series classification:
Training a single network to classify all datasets in the archive with one
network. We are among the first to evaluate the method on the latest 2018
release of the UCR archive, a well established time series classification
benchmarking dataset.

negative:
Encoder-decoder recurrent neural network models (RNN Seq2Seq) have achieved
great success in ubiquitous areas of computation and applications. It was shown
to be successful in modeling data with both temporal and spatial dependencies
for translation or prediction tasks. In this study, we propose an embedding
approach to visualize and interpret the representation of data by these models.
Furthermore, we show that the embedding is an effective method for unsupervised
learning and can be utilized to estimate the optimality of model training. In
particular, we demonstrate that embedding space projections of the decoder
states of RNN Seq2Seq model trained on sequences prediction are organized in
clusters capturing similarities and differences in the dynamics of these
sequences. Such performance corresponds to an unsupervised clustering of any
spatio-temporal features and can be employed for time-dependent problems such
as temporal segmentation, clustering of dynamic activity, self-supervised
classification, action recognition, failure prediction, etc. We test and
demonstrate the application of the embedding methodology to time-sequences of
3D human body poses. We show that the methodology provides a high-quality
unsupervised categorization of movements.

negative:
Temporal knowledge graph (TKG) reasoning is a crucial task that has gained
increasing research interest in recent years. Most existing methods focus on
reasoning at past timestamps to complete the missing facts, and there are only
a few works of reasoning on known TKGs to forecast future facts. Compared with
the completion task, the forecasting task is more difficult that faces two main
challenges: (1) how to effectively model the time information to handle future
timestamps? (2) how to make inductive inference to handle previously unseen
entities that emerge over time? To address these challenges, we propose the
first reinforcement learning method for forecasting. Specifically, the agent
travels on historical knowledge graph snapshots to search for the answer. Our
method defines a relative time encoding function to capture the timespan
information, and we design a novel time-shaped reward based on Dirichlet
distribution to guide the model learning. Furthermore, we propose a novel
representation method for unseen entities to improve the inductive inference
ability of the model. We evaluate our method for this link prediction task at
future timestamps. Extensive experiments on four benchmark datasets demonstrate
substantial performance improvement meanwhile with higher explainability, less
calculation, and fewer parameters when compared with existing state-of-the-art
methods.

negative:
The ability to decompose scenes in terms of abstract building blocks is
crucial for general intelligence. Where those basic building blocks share
meaningful properties, interactions and other regularities across scenes, such
decompositions can simplify reasoning and facilitate imagination of novel
scenarios. In particular, representing perceptual observations in terms of
entities should improve data efficiency and transfer performance on a wide
range of tasks. Thus we need models capable of discovering useful
decompositions of scenes by identifying units with such regularities and
representing them in a common format. To address this problem, we have
developed the Multi-Object Network (MONet). In this model, a VAE is trained
end-to-end together with a recurrent attention network -- in a purely
unsupervised manner -- to provide attention masks around, and reconstructions
of, regions of images. We show that this model is capable of learning to
decompose and represent challenging 3D scenes into semantically meaningful
components, such as objects and background elements.

negative:
Time-series forecasting is important for many applications. Forecasting
models are usually trained using time-series data in a specific target task.
However, sufficient data in the target task might be unavailable, which leads
to performance degradation. In this paper, we propose a few-shot learning
method that forecasts a future value of a time-series in a target task given a
few time-series in the target task. Our model is trained using time-series data
in multiple training tasks that are different from target tasks. Our model uses
a few time-series to build a forecasting function based on a recurrent neural
network with an attention mechanism. With the attention mechanism, we can
retrieve useful patterns in a small number of time-series for the current
situation. Our model is trained by minimizing an expected test error of
forecasting next timestep values. We demonstrate the effectiveness of the
proposed method using 90 time-series datasets.

negative:
The problem of missing values in multivariable time series is a key challenge
in many applications such as clinical data mining. Although many imputation
methods show their effectiveness in many applications, few of them are designed
to accommodate clinical multivariable time series. In this work, we propose a
multiple imputation model that capture both cross-sectional information and
temporal correlations. We integrate Gaussian processes with mixture models and
introduce individualized mixing weights to handle the variance of predictive
confidence of Gaussian process models. The proposed model is compared with
several state-of-the-art imputation algorithms on both real-world and synthetic
datasets. Experiments show that our best model can provide more accurate
imputation than the benchmarks on all of our datasets.

negative:
Additive Manufacturing (AM) is a crucial component of the smart industry. In
this paper, we propose an automated quality grading system for the AM process
using a deep convolutional neural network (CNN) model. The CNN model is trained
offline using the images of the internal and surface defects in the
layer-by-layer deposition of materials and tested online by studying the
performance of detecting and classifying the failure in AM process at different
extruder speeds and temperatures. The model demonstrates the accuracy of 94%
and specificity of 96%, as well as above 75% in three classifier measures of
the Fscore, the sensitivity, and precision for classifying the quality of the
printing process in five grades in real-time. The proposed online model adds an
automated, consistent, and non-contact quality control signal to the AM process
that eliminates the manual inspection of parts after they are entirely built.
The quality monitoring signal can also be used by the machine to suggest
remedial actions by adjusting the parameters in real-time. The proposed quality
predictive model serves as a proof-of-concept for any type of AM machines to
produce reliable parts with fewer quality hiccups while limiting the waste of
both time and materials.

negative:
Data analysis has become a necessity in the modern era of cricket. Everything
from effective team management to match win predictions use some form of
analytics. Meaningful data representations are necessary for efficient analysis
of data. In this study we investigate the use of adaptive (learnable)
embeddings to represent inter-related features (such as players, teams, etc).
The data used for this study is collected from a classical T20 tournament IPL
(Indian Premier League). To naturally facilitate the learning of meaningful
representations of features for accurate data analysis, we formulate a deep
representation learning framework which jointly learns a custom set of
embeddings (which represents our features of interest) through the minimization
of a contrastive loss. We base our objective on a set of classes obtained as a
result of hierarchical clustering on the overall run rate of an innings. It's
been assessed that the framework ensures greater generality in the obtained
embeddings, on top of which a task based analysis of overall run rate
prediction was done to show the reliability of the framework.

negative:
Pointwise matches between two time series are of great importance in time
series analysis, and dynamic time warping (DTW) is known to provide generally
reasonable matches. There are situations where time series alignment should be
invariant to scaling and offset in amplitude or where local regions of the
considered time series should be strongly reflected in pointwise matches. Two
different variants of DTW, affine DTW (ADTW) and regional DTW (RDTW), are
proposed to handle scaling and offset in amplitude and provide regional
emphasis respectively. Furthermore, ADTW and RDTW can be combined in two
different ways to generate alignments that incorporate advantages from both
methods, where the affine model can be applied either globally to the entire
time series or locally to each region. The proposed alignment methods
outperform DTW on specific simulated datasets, and one-nearest-neighbor
classifiers using their associated difference measures are competitive with the
difference measures associated with state-of-the-art alignment methods on real
datasets.

negative:
We study the settings for which deep contextual embeddings (e.g., BERT) give
large improvements in performance relative to classic pretrained embeddings
(e.g., GloVe), and an even simpler baseline---random word embeddings---focusing
on the impact of the training set size and the linguistic properties of the
task. Surprisingly, we find that both of these simpler baselines can match
contextual embeddings on industry-scale data, and often perform within 5 to 10%
accuracy (absolute) on benchmark tasks. Furthermore, we identify properties of
data for which contextual embeddings give particularly large gains: language
containing complex structure, ambiguous word usage, and words unseen in
training.

negative:
This paper discusses the prediction of hierarchical time series, where each
upper-level time series is calculated by summing appropriate lower-level time
series. Forecasts for such hierarchical time series should be coherent, meaning
that the forecast for an upper-level time series equals the sum of forecasts
for corresponding lower-level time series. Previous methods for making coherent
forecasts consist of two phases: first computing base (incoherent) forecasts
and then reconciling those forecasts based on their inherent hierarchical
structure. With the aim of improving time series predictions, we propose a
structured regularization method for completing both phases simultaneously. The
proposed method is based on a prediction model for bottom-level time series and
uses a structured regularization term to incorporate upper-level forecasts into
the prediction model. We also develop a backpropagation algorithm specialized
for application of our method to artificial neural networks for time series
prediction. Experimental results using synthetic and real-world datasets
demonstrate the superiority of our method in terms of prediction accuracy and
computational efficiency.

negative:
Leveled Homomorphic Encryption (LHE) offers a potential solution that could
allow sectors with sensitive data to utilize the cloud and securely deploy
their models for remote inference with Deep Neural Networks (DNN). However,
this application faces several obstacles due to the limitations of LHE. One of
the main problems is the incompatibility of commonly used nonlinear functions
in DNN with the operations supported by LHE, i.e. addition and multiplication.
As common in LHE approaches, we train a model with a nonlinear function, and
replace it with a low-degree polynomial approximation at inference time on
private data. While this typically leads to approximation errors and loss in
prediction accuracy, we propose a method that reduces this loss to small values
or eliminates it entirely, depending on simple hyper-parameters. This is
achieved by the introduction of a novel and elegantly simple Min-Max
normalization scheme, which scales inputs to nonlinear functions into ranges
with low approximation error. While being intuitive in its concept and trivial
to implement, we empirically show that it offers a stable and effective
approximation solution to nonlinear functions in DNN. In return, this can
enable deeper networks with LHE, and facilitate the development of security-
and privacy-aware analytics applications.

negative:
For autonomous vehicles (AVs) to behave appropriately on roads populated by
human-driven vehicles, they must be able to reason about the uncertain
intentions and decisions of other drivers from rich perceptual information.
Towards these capabilities, we present a probabilistic forecasting model of
future interactions between a variable number of agents. We perform both
standard forecasting and the novel task of conditional forecasting, which
reasons about how all agents will likely respond to the goal of a controlled
agent (here, the AV). We train models on real and simulated data to forecast
vehicle trajectories given past positions and LIDAR. Our evaluation shows that
our model is substantially more accurate in multi-agent driving scenarios
compared to existing state-of-the-art. Beyond its general ability to perform
conditional forecasting queries, we show that our model's predictions of all
agents improve when conditioned on knowledge of the AV's goal, further
illustrating its capability to model agent interactions.

negative:
Additive Manufacturing (AM) is a manufacturing paradigm that builds
three-dimensional objects from a computer-aided design model by successively
adding material layer by layer. AM has become very popular in the past decade
due to its utility for fast prototyping such as 3D printing as well as
manufacturing functional parts with complex geometries using processes such as
laser metal deposition that would be difficult to create using traditional
machining. As the process for creating an intricate part for an expensive metal
such as Titanium is prohibitive with respect to cost, computational models are
used to simulate the behavior of AM processes before the experimental run.
However, as the simulations are computationally costly and time-consuming for
predicting multiscale multi-physics phenomena in AM, physics-informed
data-driven machine-learning systems for predicting the behavior of AM
processes are immensely beneficial. Such models accelerate not only multiscale
simulation tools but also empower real-time control systems using in-situ data.
In this paper, we design and develop essential components of a scientific
framework for developing a data-driven model-based real-time control system.
Finite element methods are employed for solving time-dependent heat equations
and developing the database. The proposed framework uses extremely randomized
trees - an ensemble of bagged decision trees as the regression algorithm
iteratively using temperatures of prior voxels and laser information as inputs
to predict temperatures of subsequent voxels. The models achieve mean absolute
percentage errors below 1% for predicting temperature profiles for AM
processes. The code is made available for the research community at
https://github.com/paularindam/ml-iter-additive.

negative:
Understanding customer behavior is fundamental for many use-cases in
industry, especially in accelerated growth areas such as fin-tech and
e-commerce. Structured data are often expensive, time-consuming and inadequate
to analyze and study complex customer behaviors. In this paper, we propose a
multi-graph embedding approach for creating a non-linear representation of
customers in order to have a better knowledge of their characteristics without
having any prior information about their financial status or their interests.
By applying the current method we are able to predict users' future behavior
with a reasonably high accuracy only by having the information of their
friendship network. Potential applications include recommendation systems and
credit risk forecasting.

negative:
The key contribution of this paper is to propose a classification into two
dimensions of the load forecasting studies to decide which forecasting tools to
use in which case. This classification aims to provide a synthetic view of the
relevant forecasting techniques and methodologies by forecasting problem. In
addition, the key principles of the main techniques and methodologies used are
summarized along with the reviews of these papers.
  The classification process relies on two couples of parameters that define a
forecasting problem. Each article is classified with key information about the
dataset used and the forecasting tools implemented: the forecasting techniques
(probabilistic or deterministic) and methodologies, the data cleansing
techniques, and the error metrics.
  The process to select the articles reviewed in this paper was conducted into
two steps. First, a set of load forecasting studies was built based on relevant
load forecasting reviews and forecasting competitions. The second step
consisted in selecting the most relevant studies of this set based on the
following criteria: the quality of the description of the forecasting
techniques and methodologies implemented, the description of the results, and
the contributions.
  This paper can be read in two passes. The first one by identifying the
forecasting problem of interest to select the corresponding class into one of
the four classification tables. Each one references all the articles classified
across a forecasting horizon. They provide a synthetic view of the forecasting
tools used by articles addressing similar forecasting problems. Then, a second
level composed of four Tables summarizes key information about the forecasting
tools and the results of these studies. The second pass consists in reading the
key principles of the main techniques and methodologies of interest and the
reviews of the articles.

negative:
This paper introduces the concept of travel behavior embeddings, a method for
re-representing discrete variables that are typically used in travel demand
modeling, such as mode, trip purpose, education level, family type or
occupation. This re-representation process essentially maps those variables
into a latent space called the \emph{embedding space}. The benefit of this is
that such spaces allow for richer nuances than the typical transformations used
in categorical variables (e.g. dummy encoding, contrasted encoding, principal
components analysis). While the usage of latent variable representations is not
new per se in travel demand modeling, the idea presented here brings several
innovations: it is an entirely data driven algorithm; it is informative and
consistent, since the latent space can be visualized and interpreted based on
distances between different categories; it preserves interpretability of
coefficients, despite being based on Neural Network principles; and it is
transferrable, in that embeddings learned from one dataset can be reused for
other ones, as long as travel behavior keeps consistent between the datasets.
  The idea is strongly inspired on natural language processing techniques,
namely the word2vec algorithm. Such algorithm is behind recent developments
such as in automatic translation or next word prediction. Our method is
demonstrated using a model choice model, and shows improvements of up to 60\%
with respect to initial likelihood, and up to 20% with respect to likelihood of
the corresponding traditional model (i.e. using dummy variables) in
out-of-sample evaluation. We provide a new Python package, called PyTre (PYthon
TRavel Embeddings), that others can straightforwardly use to replicate our
results or improve their own models. Our experiments are themselves based on an
open dataset (swissmetro).

negative:
Multi-dimensional data exploration is a classic research topic in
visualization. Most existing approaches are designed for identifying record
patterns in dimensional space or subspace. In this paper, we propose a visual
analytics approach to exploring subset patterns. The core of the approach is a
subset embedding network (SEN) that represents a group of subsets as
uniformly-formatted embeddings. We implement the SEN as multiple subnets with
separate loss functions. The design enables to handle arbitrary subsets and
capture the similarity of subsets on single features, thus achieving accurate
pattern exploration, which in most cases is searching for subsets having
similar values on few features. Moreover, each subnet is a fully-connected
neural network with one hidden layer. The simple structure brings high training
efficiency. We integrate the SEN into a visualization system that achieves a
3-step workflow. Specifically, analysts (1) partition the given dataset into
subsets, (2) select portions in a projected latent space created using the SEN,
and (3) determine the existence of patterns within selected subsets. Generally,
the system combines visualizations, interactions, automatic methods, and
quantitative measures to balance the exploration flexibility and operation
efficiency, and improve the interpretability and faithfulness of the identified
patterns. Case studies and quantitative experiments on multiple open datasets
demonstrate the general applicability and effectiveness of our approach.

negative:
The reconstruction of phase spaces is an essential step to analyze time
series according to Dynamical System concepts. A regression performed on such
spaces unveils the relationships among system states from which we can derive
their generating rules, that is, the most probable set of functions responsible
for generating observations along time. In this sense, most approaches rely on
Takens' embedding theorem to unfold the phase space, which requires the
embedding dimension and the time delay. Moreover, although several methods have
been proposed to empirically estimate those parameters, they still face
limitations due to their lack of consistency and robustness, which has
motivated this paper. As an alternative, we here propose an artificial neural
network with a forgetting mechanism to implicitly learn the phase spaces
properties, whatever they are. Such network trains on forecasting errors and,
after converging, its architecture is used to estimate the embedding
parameters. Experimental results confirm that our approach is either as
competitive as or better than most state-of-the-art strategies while revealing
the temporal relationship among time-series observations.

negative:
Low-dimensional embeddings, from classical spectral embeddings to modern
neural-net-inspired methods, are a cornerstone in the modeling and analysis of
complex networks. Recent work by Seshadhri et al. (PNAS 2020) suggests that
such embeddings cannot capture local structure arising in complex networks. In
particular, they show that any network generated from a natural low-dimensional
model cannot be both sparse and have high triangle density (high clustering
coefficient), two hallmark properties of many real-world networks.
  In this work we show that the results of Seshadhri et al. are intimately
connected to the model they use rather than the low-dimensional structure of
complex networks. Specifically, we prove that a minor relaxation of their model
can generate sparse graphs with high triangle density. Surprisingly, we show
that this same model leads to exact low-dimensional factorizations of many
real-world networks. We give a simple algorithm based on logistic principal
component analysis (LPCA) that succeeds in finding such exact embeddings.
Finally, we perform a large number of experiments that verify the ability of
very low-dimensional embeddings to capture local structure in real-world
networks.

negative:
Growing amounts of online user data motivate the need for automated
processing techniques. In case of user ratings, one interesting option is to
use neural networks for learning to predict ratings given an item and a user.
While training for prediction, such an approach at the same time learns to map
each user to a vector, a so-called user embedding. Such embeddings can for
example be valuable for estimating user similarity. However, there are various
ways how item and user information can be combined in neural networks, and it
is unclear how the way of combining affects the resulting embeddings. In this
paper, we run an experiment on movie ratings data, where we analyze the effect
on embedding quality caused by several fusion strategies in neural networks.
For evaluating embedding quality, we propose a novel measure, Pair-Distance
Correlation, which quantifies the condition that similar users should have
similar embedding vectors. We find that the fusion strategy affects results in
terms of both prediction performance and embedding quality. Surprisingly, we
find that prediction performance not necessarily reflects embedding quality.
This suggests that if embeddings are of interest, the common tendency to select
models based on their prediction ability should be reconsidered.

negative:
How will my face look when I get older? Or, for a more challenging question:
How will my brain look when I get older? To answer this question one must
devise (and learn from data) a multivariate auto-regressive function which
given an image and a desired target age generates an output image. While
collecting data for faces may be easier, collecting longitudinal brain data is
not trivial. We propose a deep learning-based method that learns to simulate
subject-specific brain ageing trajectories without relying on longitudinal
data. Our method synthesises images conditioned on two factors: age (a
continuous variable), and status of Alzheimer's Disease (AD, an ordinal
variable). With an adversarial formulation we learn the joint distribution of
brain appearance, age and AD status, and define reconstruction losses to
address the challenging problem of preserving subject identity. We compare with
several benchmarks using two widely used datasets. We evaluate the quality and
realism of synthesised images using ground-truth longitudinal data and a
pre-trained age predictor. We show that, despite the use of cross-sectional
data, our model learns patterns of gray matter atrophy in the middle temporal
gyrus in patients with AD. To demonstrate generalisation ability, we train on
one dataset and evaluate predictions on the other. In conclusion, our model
shows an ability to separate age, disease influence and anatomy using only 2D
cross-sectional data that should be useful in large studies into
neurodegenerative disease, that aim to combine several data sources. To
facilitate such future studies by the community at large our code is made
available at https://github.com/xiat0616/BrainAgeing.

negative:
Quantum computers are expected to surpass the computational capabilities of
classical computers during this decade, and achieve disruptive impact on
numerous industry sectors, particularly finance. In fact, finance is estimated
to be the first industry sector to benefit from Quantum Computing not only in
the medium and long terms, but even in the short term. This review paper
presents the state of the art of quantum algorithms for financial applications,
with particular focus to those use cases that can be solved via Machine
Learning.

negative:
The lack of a large amount of training data has always been the constraining
factor in solving a lot of problems in machine learning, making One Shot
Learning one of the most intriguing ideas in machine learning. It aims to learn
information about object categories from one, or only a few training examples.
This process of learning in deep learning is usually accomplished by proper
objective function, i.e; loss function and embeddings extraction i.e;
architecture. In this paper, we discussed about metrics based deep learning
architectures for one shot learning such as Siamese neural networks and present
a method to improve on their accuracy using Kafnets (kernel-based
non-parametric activation functions for neural networks) by learning proper
embeddings with relatively less number of epochs. Using kernel activation
functions, we are able to achieve strong results which exceed those of ReLU
based deep learning models in terms of embeddings structure, loss convergence,
and accuracy.

negative:
We present an instance-based nearest neighbor approach to entity linking. In
contrast to most prior entity retrieval systems which represent each entity
with a single vector, we build a contextualized mention-encoder that learns to
place similar mentions of the same entity closer in vector space than mentions
of different entities. This approach allows all mentions of an entity to serve
as "class prototypes" as inference involves retrieving from the full set of
labeled entity mentions in the training set and applying the nearest mention
neighbor's entity label. Our model is trained on a large multilingual corpus of
mention pairs derived from Wikipedia hyperlinks, and performs nearest neighbor
inference on an index of 700 million mentions. It is simpler to train, gives
more interpretable predictions, and outperforms all other systems on two
multilingual entity linking benchmarks.

negative:
We address the problem of solving mixed random linear equations. We have
unlabeled observations coming from multiple linear regressions, and each
observation corresponds to exactly one of the regression models. The goal is to
learn the linear regressors from the observations. Classically, Alternating
Minimization (AM) (which is a variant of Expectation Maximization (EM)) is used
to solve this problem. AM iteratively alternates between the estimation of
labels and solving the regression problems with the estimated labels.
Empirically, it is observed that, for a large variety of non-convex problems
including mixed linear regression, AM converges at a much faster rate compared
to gradient based algorithms. However, the existing theory suggests similar
rate of convergence for AM and gradient based methods, failing to capture this
empirical behavior. In this paper, we close this gap between theory and
practice for the special case of a mixture of $2$ linear regressions. We show
that, provided initialized properly, AM enjoys a \emph{super-linear} rate of
convergence in certain parameter regimes. To the best of our knowledge, this is
the first work that theoretically establishes such rate for AM. Hence, if we
want to recover the unknown regressors upto an error (in $\ell_2$ norm) of
$\epsilon$, AM only takes $\mathcal{O}(\log \log (1/\epsilon))$ iterations.
Furthermore, we compare AM with a gradient based heuristic algorithm
empirically and show that AM dominates in iteration complexity as well as
wall-clock time.