LLMs Enable Grounded, Automated Explanations for Time Series Forecasts
A new framework leverages large language models (LLMs) to generate natural language explanations for time series forecasts in a domain-agnostic manner. By extracting structured explanatory factors from historical analyst-written explanations and constraining generation to verifiable evidence, the approach reduces hallucination. Evaluations on financial and freight pricing datasets show that the generated explanations closely match analyst-written ones in readability, consistency, and persuasiveness.
Why it matters: This work demonstrates scalable, automated generation of high-quality, grounded explanations for time series forecasts without the need for domain-specific fine-tuning, potentially streamlining decision-making in critical domains.
Full story at: arXiv AI/ML ↗