The study and modeling of systems have called the attention of several researchers, who
are interested in estimating rules to describe data behavior. However, before proceeding …

Time series tools are part and parcel of modern day research. Their usage in the biomedical
field; specifically, in neuroscience, has not been previously quantified. A quantification of …

tslearn is a general-purpose Python machine learning library for time series that offers tools
for pre-processing and feature extraction as well as dedicated models for clustering …

The behavior of many complex physical systems is affected by a variety of phenomena
occurring at different temporal scales. Time series data produced by measuring properties of …

Data Science students and practitioners want to find a forecast that “works” and don't want to
be constrained to a single forecasting strategy, Practical Time Series Analysis for Data …

Synthetic data generation describes the process of learning the underlying distribution of a
given real dataset in a model, which is, in turn, sampled to produce new data objects still …

The information technology revolution, especially with the adoption of the Internet of Things,
longitudinal data in many domains become more available and accessible for secondary …

Enormous amounts of time series are being collected in many different domains. These
include, but are not limited to, aviation, computing, energy, finance, logistics, and medicine …

The four time-series software packages reviewed here have a strong econometric
orientation, though all offer plenty for the general time-series analyst. Of the four, TSP 4.2 B …