Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop

• URL: http://arxiv.org/abs/2503.01013v1
• Date: Sun, 02 Mar 2025 20:40:53 GMT
• Title: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
• Authors: Yushan Jiang, Wenchao Yu, Geon Lee, Dongjin Song, Kijung Shin, Wei Cheng, Yanchi Liu, Haifeng Chen,
• Abstract summary: TimeXL is a multi-modal prediction framework that integrates a prototype-based time series encoder.<n>It produces more accurate predictions and interpretable explanations.<n> Empirical evaluations on four real-world datasets demonstrate that TimeXL achieves up to 8.9% improvement in AUC.
• Score: 63.34626300024294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual signals present in auxiliary modalities. To bridge this gap, we introduce TimeXL, a multi-modal prediction framework that integrates a prototype-based time series encoder with three collaborating Large Language Models (LLMs) to deliver more accurate predictions and interpretable explanations. First, a multi-modal prototype-based encoder processes both time series and textual inputs to generate preliminary forecasts alongside case-based rationales. These outputs then feed into a prediction LLM, which refines the forecasts by reasoning over the encoder's predictions and explanations. Next, a reflection LLM compares the predicted values against the ground truth, identifying textual inconsistencies or noise. Guided by this feedback, a refinement LLM iteratively enhances text quality and triggers encoder retraining. This closed-loop workflow -- prediction, critique (reflect), and refinement -- continuously boosts the framework's performance and interpretability. Empirical evaluations on four real-world datasets demonstrate that TimeXL achieves up to 8.9\% improvement in AUC and produces human-centric, multi-modal explanations, highlighting the power of LLM-driven reasoning for time series prediction.

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