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

Understand

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.