Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …

Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Pre-training on time series poses a unique challenge due to the potential mismatch between
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …

The vast majority of time-series forecasting approaches require a substantial training
dataset. However, many real-life forecasting applications have very little initial observations …

Time series has wide applications in the real world and is known to be difficult to forecast.
Since its statistical properties change over time, its distribution also changes temporally …

This paper brings deep learning at the forefront of research into time series classification
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …

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 …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

To understand human behavior and intrinsically anticipate human intentions, research into
human activity recognition HAR) using sensors in wearable and handheld devices has …