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

Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …

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

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Time series analysis is essential for comprehending the complexities inherent in various real-
world systems and applications. Although large language models (LLMs) have recently …

Multivariate Time-Series (MTS) data is crucial in various application fields. With its
sequential and multi-source (multiple sensors) properties, MTS data inherently exhibits …

Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …

Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for
spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic …

The study of time series is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …

Deep Learning has been successfully applied to many application domains, yet its
advantages have been slow to emerge for time series forecasting. For example, in the well …