🤖 AI Summary
This work addresses the “last-mile” gap between statistical time series forecasting and business decision-making—specifically, the challenge of effectively incorporating weakly structured contextual factors such as holidays and marketing campaigns. To bridge this gap, we propose the first framework that systematically integrates large language model (LLM) agents into the post-forecasting refinement stage. Our approach unifies the forecasting workspace, leverages tool-augmented retrieval of external evidence, and translates LLM reasoning into explicit revision operations under structured safety constraints. It further supports Map-Reduce-style long-horizon divide-and-conquer forecasting and incorporates a memory-based reflection mechanism. Evaluated in real-world business settings, the method significantly enhances the operational relevance and decision utility of forecasts, delivering controllable, auditable, and business-ready predictions.
📝 Abstract
Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation. However, in real-world forecasting settings, a statistically plausible baseline is rarely the final forecast used in practice. Before a forecast becomes decision-ready, it often needs to be revised using weakly structured business context such as holiday effects, campaign plans, external events, historical analogs, and expert feedback. This practical stage remains underexplored in the forecasting literature. In this paper, we formulate this stage as the \textbf{last-mile forecasting} problem and present an LLM-agent framework that sits on top of a forecasting backbone. Our system maintains a unified forecast workspace, invokes tools to retrieve contextual evidence, and converts reasoning trajectories into explicit forecast revision actions under structural safety constraints. It also supports long-horizon forecasting through map-reduce-style decomposition and post-hoc reflection through a memory bank. The resulting system is designed to be controllable and auditable. Through real-world case studies, we show how LLM agents can bridge the gap between statistical prediction and business-ready forecasting.