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

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, 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 …

Missing data is a pervasive issue in both scientific and engineering tasks, especially for the
modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank …

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 …

Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …

Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in
diverse applications, such as air pollution monitoring, disease tracking, and cloud-demand …

Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …