FreSia: Frequency-Semantic Instantiation and Alignment for Multivariate Time Series Analysis

Excerpt

arXiv:2610.05726v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential in multivariate time series forecasting and anomaly detection. Existing studies predominantly inject temporal information into LLMs via direct numerical tokenization or heuristic textual descriptions. However, LLMs still face difficulty in perceiving the underlying structural patterns of numerical time series, particularly the seasonal and trend components obscured by discrete numerical token