FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting
Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data. To enable prompt-free, frequency-aware adaptation of frozen LLMs, we propose FM-LLM (Frequency-Enhanced Mixture-of-Experts for adapting LLMs to Time Series Forecasting), an autoreg
Record details
Published: 12 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 13 August 2026
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ethics.ai (12 August 2026), “FM-LLM: A frequency-enhanced mixture-of-experts framework for adapting LLMs to time series forecasting,” evidence record 18798, https://ethics.ai/record/18798 (originally published by arXiv).
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