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 Extrinsic Regression (TSER) involves using a set of training time series
to form a predictive model of a continuous response variable that is not directly related to the …

A time series is a sequence of sequentially ordered real values in time. Time series
classification (TSC) is the task of assigning a time series to one of a set of predefined …

The measurement of progress using benchmarks evaluations is ubiquitous in computer
science and machine learning. However, common approaches to analyzing and presenting …

Foundation models, especially LLMs, are profoundly transforming deep learning. Instead of
training many task-specific models, we can adapt a single pretrained model to many tasks …

This paper introduces ConvTimeNet, a novel deep hierarchical fully convolutional network
designed to serve as a general-purpose model for time series analysis. The key design of …

Shapelets and CNN are two typical approaches to model time series. Shapelets aim at
finding a set of sub-sequences that extract feature-based interpretable shapes, but may …

Time series classification is an important and challenging problem in data analysis. With the
increase in time series data availability, hundreds of algorithms have been proposed. A …

Advancements in self-supervised pre-training (SSL) have significantly advanced the field of
learning transferable time series representations, which can be very useful in enhancing the …

Over the past decade, Time Series Classification (TSC) has gained an increasing attention.
While various methods were explored, deep learning–particularly through Convolutional …