SOURCE-LINKED INTELLIGENCE

Compositional Spectral Prompts for LLM-based Online Time Series Forecasting

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

To address the sequential and evolving nature of time series, the Online Time Series Forecasting (OTSF) task has been extensively studied in multiple domains. Existing research focuses on adapting to non-stationary environments by employing memory buffer-based retrieval strategies. However, we observe that such frameworks struggle with long-term adaptation and fail to generalize to unseen patterns. To this end, we introduce CoSPOT, an LLM-based online time series forecasting framework that leverages a pre-trained LLM as the backbone online forecaster, motivated by its strong few-shot capabilit

Read original source ↗ Open in workspace

recordType

paper

region

Global