<ul><li>Conventional forecasting methods are limited by unimodal time series data, hindering the utilization of textual information.</li><li>Integrating large language models (LLMs) and time series foundation models (TSFMs) has become a crucial research challenge for improved future inference.</li><li>ChronoSteer, a proposed multimodal model, combines LLM for textual event transformation and TSFM for temporal modeling by using revision instructions.</li><li>By employing a two-stage training strategy with synthetic data, ChronoSteer achieves a 25.7% enhancement in prediction accuracy over the unimodal backbone and a 22.5% improvement compared to the prior state-of-the-art multimodal method.</li></ul>

ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data

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