Using LLMs for Explainable, Data-Driven Insight Generation from Time Series
Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations. Producing such explanations is usually a manual and costly process, and attempts to automate it using large language models often suffer from hallucination when applied to temporal data. We propose a domain-agnostic framework for grounded natural language explanation generation for time series forecasts, illustrated in Figure 1. The framework consists of three components: (i) extraction of structured explanatory factors from historical analyst-written explanations, (ii) evidence-conditioned explanation generation, and (iii) scalable evaluation for readabili...
| Authors | Ria Mundhra, Gustavo Sato dos Santos, Michael Benedikt |
|---|---|
| Primary category | cs.AI |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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