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Compositional Spectral Prompts for LLM-based Online Time Series Forecasting
AI InsightThis research proposes freezing LLM parameters and using spectral prompts for online time series forecasting. This implies large models are expanding from text to structured data analysis, and adapting to non-stationary environments without fine-tuning validates the potential of LLMs as general-purpose forecasting backbones.Key TakeawayLLM time series forecasting is shifting from full fine-tuning to lightweight adaptation via frozen parameters and spectral prompts.Why It MattersOnline time series forecasting is often limited by long-term adaptation in non-stationary environments. Leveraging LLM's few-shot capabilities with spectral prompts reduces adaptation costs, offering a new paradigm for financial and industrial data applications.Who's Affected- Quantitative AnalystsIf generalizable, it offers a low-fine-tuning-cost dynamic forecasting solution for high-frequency trading.
- AI ResearchersValidates frequency-domain prompts in LLM structured data modeling, expanding prompt engineering boundaries.
What's NextObserve its performance on real industrial data, specifically its accuracy and latency in generalizing to unseen patterns compared to traditional time series models.Importance 40/100