aiqbench analysis
CTRL introduces a novel framework that separates semantic reasoning from quantitative prediction in time series forecasting by using LLM agents as controllers for error correction. This approach enhances robustness against non-stationary dynamics and distribution shifts while maintaining efficiency through label-free test-time adaptation and minimal LLM calls. The method represents a significant step forward in leveraging large language models for specialized machine learning tasks beyond general text generation.
CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning
arXiv:2609.23257v1 Announce Type: cross Abstract: Time series forecasting underpins critical decision-making across diverse domains. While large language models (LLMs) offer promising reasoning capabilities, existing LLM-based time series forecasting approaches either reduce them to numerical predictors that bypass their strengths, or allow direct forecast generation that destabilizes predictions in non-stationary settings. We introduce CTRL, a framework that decouples semantic reasoning from quantitative prediction. A frozen backbone generates base forecasts, while specialized LLM agents function as controllers that analyze backbone prediction errors through decomposed trend, seasonal, and irregular components, grounding reasoning in interpretable temporal structure. Each agent outputs compact control signals that a lightweight residual decoder translates into forecast corrections. CTRL incorporates label-free test-time adaptation that detects distribution shift from input statistics alone and readapts control signals with only 3-24 LLM calls via caching. CTRL is explicitly designed to improve robustness under non-stationary temporal dynamics and distribution shift, while remaining competitive on highly stationary time series where adaptive correction provides limited additional benefit.
Abstract:Time series forecasting underpins critical decision-making across diverse domains. While large language models (LLMs) offer promising reasoning capabilities, existing LLM-based time series forecasting approaches either reduce them to numerical predictors that bypass their strengths, or allow direct forecast generation that destabilizes predictions in non-stationary settings. We introduce CTRL, a framework that decouples semantic reasoning from quantitative prediction. A frozen backbone generates base forecasts, while specialized LLM agents function as controllers that analyze backbone prediction errors through decomposed trend, seasonal, and irregular components, grounding reasoning in interpretable temporal structure. Each agent outputs compact control signals that a lightweight residual decoder translates into forecast corrections. CTRL incorporates label-free test-time adaptation that detects distribution shift from input statistics alone and readapts control signals with only 3-24 LLM calls via caching. CTRL is explicitly designed to improve robustness under non-stationary temporal dynamics and distribution shift, while remaining competitive on highly stationary time series where adaptive correction provides limited additional benefit.
Published in Findings of the Association for Computational Linguistics: ACL 2026. 18 pages, 9 figures, 22 tables
Machine Learning (cs.LG); Computation and Language (cs.CL)
I.2.6; I.2.7; G.3
arXiv:2609.23257 [cs.LG]
(or arXiv:2609.23257v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.23257 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 274 words
