Training-Free Continual Learning for Multimodal Forecasting via Prompt Evolution
Read the original on arXiv AI →The paper proposes a training‑free continual learning approach for multimodal time‑series forecasting by evolving a textual guidance block that is appended to a frozen large language model’s prompt. On a benchmark combining weekly search indices, daily currencies, and daily commodities with web‑retrieved news, the evolved 900‑word guidance outperforms existing time‑series foundation models and transfers to newer models without loss, while requiring only monetary cost rather than GPU days. The authors also show that compiling the guidance into executable code recovers most of the gain deterministically and that the advantage grows at short horizons, with news text being distilled into policy during learning.
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