3 papers
cs.LG2026
What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
Fan Zhang, Shiming Fan, Hua Wang
Multivariate time series forecasting is critical in many real-world systems, and thus modeling cross-channel dependencies is essential. Although existing methods improve overall ac…
cs.LG2026
TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting
Fan Zhang, Shiming Fan, Hua Wang
Despite the recent success of large language models (LLMs) in time-series forecasting, most existing methods still adopt a Deep Synchronous Fusion strategy, where dense interaction…
cs.LG2026
Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting
Fan Zhang, Shiming Fan, Hua Wang
Time series forecasting is crucial for the World Wide Web and represents a core technical challenge in ensuring the stable and efficient operation of modern web services, such as i…