Bridging the Last Mile of Time Series Forecasting with LLM Agents

• URL: http://arxiv.org/abs/2606.02497v1
• Date: Mon, 01 Jun 2026 17:03:40 GMT
• Title: Bridging the Last Mile of Time Series Forecasting with LLM Agents
• Abstract summary: In real-world forecasting settings, a statistically plausible baseline is rarely the final forecast used in practice.<n>This practical stage remains underexplored in the forecasting literature.<n>We present an LLM-agent framework that sits on top of a forecasting backbone.
• Score: 0.6416429054645992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting [28.10023756949099]
  AuditForecast is an agentic scaffold for structured probabilistic forecasting.<n>It anchors the forecast with a suitable quantitative baseline model, uses model-guided data retrieval to derive a base probability, and then applies situational factor updates outside the model's scope.<n> AuditForecast produces an auditable forecasting report that makes forecast construction explicit and supports systematic post hoc analysis.
  arXiv  Detail & Related papers  (2026-09-05T05:58:39Z)
• LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications [76.41731220830714]
  Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tools, and iterative prediction.<n>We examine applications in finance, weather, health, energy, and operations, and we summarize the benchmarks and datasets used for evaluation.
  arXiv  Detail & Related papers  (2026-08-24T10:02:39Z)
• Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting [81.44658512649633]
  KUP-BI is a new time-series modeling paradigm that distills continuation-style knowledge from a emphtrain-only historical library.<n>It consistently improves the forecasting performance of state-of-the-art models, with small additional overhead.
  arXiv  Detail & Related papers  (2026-05-19T01:43:00Z)
• Nexus : An Agentic Framework for Time Series Forecasting [72.73790673303154]
  Time series forecasting requires reasoning with unstructured contextual data such as news or events.<n>We introduce Nexus, a multi-agent forecasting framework that decomposes prediction into specialized stages.<n>We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized.
  arXiv  Detail & Related papers  (2026-05-14T05:12:13Z)
• LEAF: A Living Benchmark for Event-Augmented Forecasting [78.94019460534442]
  LEAF is the first living benchmark for event-augmented forecasting tasks.<n>We evaluate state-of-the-art proprietary and open-weight LLMs.<n>We find that LLMs achieve better performance on equities they confidently identify as more predictable.
  arXiv  Detail & Related papers  (2026-05-09T03:17:59Z)
• CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting [74.29761960394848]
  We propose CastFlow, a dynamic agentic forecasting framework for large language models (LLMs)<n>CastFlow organizes the forecasting process into planning, action, forecasting, and reflection, establishing an agentic workflow.<n>Third, CastFlow adopts a role-specialized design that combines general-purpose reasoning with specialized numerical forecasting.
  arXiv  Detail & Related papers  (2026-04-30T13:24:42Z)
• Agentic Workflow Using RBA$_θ$ for Event Prediction [0.0]
  Wind power ramp events are difficult to forecast due to strong variability, multi-scale dynamics, and site-specific meteorological effects.<n>This paper proposes an event-first, frequency-aware forecasting paradigm that directly predicts ramp events and reconstructs the power trajectory thereafter.
  arXiv  Detail & Related papers  (2026-02-05T13:03:15Z)
• Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting [49.05788441962762]
  We argue for agentic time series forecasting (ATSF), which reframes forecasting as an agentic process composed of perception, planning, action, reflection, and memory.<n>We outline three representative implementation paradigms -- workflow-based design, agentic reinforcement learning, and a hybrid agentic workflow paradigm -- and discuss the opportunities and challenges that arise when shifting from model-centric prediction to agentic forecasting.
  arXiv  Detail & Related papers  (2026-02-02T08:01:11Z)
• Counterfactual Explanations for Time Series Forecasting [14.03870816983583]
  We formulate the novel problem of counterfactual generation for time series forecasting, and propose an algorithm, called ForecastCF. ForecastCF solves the problem by applying gradient-based perturbations to the original time series. Our results show that ForecastCF outperforms the baseline in terms of counterfactual validity and data manifold closeness.
  arXiv  Detail & Related papers  (2023-10-12T08:51:59Z)
• Streaming Motion Forecasting for Autonomous Driving [71.7468645504988]
  We introduce a benchmark that queries future trajectories on streaming data and we refer to it as "streaming forecasting" Our benchmark inherently captures the disappearance and re-appearance of agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks. We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster.
  arXiv  Detail & Related papers  (2023-10-02T17:13:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.