FLAIRR-TS -- Forecasting LLM-Agents with Iterative Refinement and Retrieval for Time Series

• URL: http://arxiv.org/abs/2508.19279v1
• Date: Sun, 24 Aug 2025 00:57:22 GMT
• Title: FLAIRR-TS -- Forecasting LLM-Agents with Iterative Refinement and Retrieval for Time Series
• Authors: Gunjan Jalori, Preetika Verma, Sercan Ö Arık,
• Abstract summary: We introduce FLAIRR-TS, a test-time prompt optimization framework thatutilizes an agentic system.<n>A Forecaster-agentgenerates forecasts using an initial prompt, which is refined by a refiner agent, in-formed by past outputs and retrieved analogs.<n>Experiments onbenchmark datasets show improved accuracyover static prompting and retrieval-augmentedbaselines.
• Score: 1.1360832156847103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series Forecasting with large languagemodels (LLMs) requires bridging numericalpatterns and natural language. Effective fore-casting on LLM often relies on extensive pre-processing and fine-tuning.Recent studiesshow that a frozen LLM can rival specializedforecasters when supplied with a carefully en-gineered natural-language prompt, but craft-ing such a prompt for each task is itself oner-ous and ad-hoc. We introduce FLAIRR-TS, atest-time prompt optimization framework thatutilizes an agentic system: a Forecaster-agentgenerates forecasts using an initial prompt,which is then refined by a refiner agent, in-formed by past outputs and retrieved analogs.This adaptive prompting generalizes across do-mains using creative prompt templates andgenerates high-quality forecasts without inter-mediate code generation.Experiments onbenchmark datasets show improved accuracyover static prompting and retrieval-augmentedbaselines, approaching the performance ofspecialized prompts.FLAIRR-TS providesa practical alternative to tuning, achievingstrong performance via its agentic approach toadaptive prompt refinement and retrieval.

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