Position: Empowering Time Series Reasoning with Multimodal LLMs

This paper examines the emerging field of time series reasoning using multimodal large language models (MLLMs), highlighting their ability to integrate diverse data types such as numerical time series, text, images, and audio for deeper insights beyond traditional forecasting. It proposes a new reasoning paradigm that goes beyond classical time series tasks to include complex functionalities like question answering, causal inference, and data generation. The paper discusses various model designs and training strategies for MLLMs, from zero-shot inference to two-stage tuning, emphasizing the importance of iterative feedback for improved performance. It also addresses current challenges such as the scarcity of multimodal datasets and the need for standardized evaluation metrics. The authors advocate for further research to enhance the trustworthiness and interpretability of MLLMs in high-stakes applications.