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 …
S Dooley, GS Khurana, C Mohapatra… - Advances in …, 2024 - proceedings.neurips.cc
The vast majority of time-series forecasting approaches require a substantial training dataset. However, many real-life forecasting applications have very little initial observations …
L Sun, Y Huang, H Wang, S Wu, Q Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …
Z Liu, W Pei, D Lan, Q Ma - Proceedings of the AAAI Conference on …, 2024 - ojs.aaai.org
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 …