Bridging the Last Mile of Time Series Forecasting with LLM Agents
Yuhua Liao, Zetian Wang, Qiangqiang Nie, Zhenhua Zhang
Why It Matters
What makes this one worth your time
This research addresses a critical gap in time series forecasting by emphasizing the importance of contextual factors, making forecasts more applicable in real-world business scenarios.
A novel LLM-agent framework enhances time series forecasting by incorporating contextual business insights.
Summary
The paper introduces a framework for 'last-mile forecasting' that utilizes LLM agents to enhance time series forecasting by integrating weakly structured business context and expert feedback into the forecasting process.
Key contributions
- Development of a unified forecast workspace for LLM agents.
- Implementation of structural safety constraints in forecast revision actions.
- Support for long-horizon forecasting through map-reduce-style decomposition.
Notable insights
- The formulation of 'last-mile forecasting' highlights the often-overlooked practical challenges in deploying statistical forecasts.
- The use of a memory bank for post-hoc reflection could improve the adaptability and accuracy of forecasts over time.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2606.02497v1 Announce Type: new 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.