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 …

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

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

Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

Evaluating the performance of graph neural networks (GNNs) is an essential task for
practical GNN model deployment and serving, as deployed GNNs face significant …

Traffic prediction, a critical component for intelligent transportation systems, endeavors to
foresee future traffic at specific locations using historical data. Although existing traffic …

Semi-supervised time-series classification could effectively alleviate the issue of lacking
labeled data. However, existing approaches usually ignore model interpretability, making it …

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