MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering

Understand

Understanding the relationship between textual news and time-series evolution is a critical yet under-explored challenge in applied data science.

• While multimodal learning has gained traction, existing multimodal time-series datasets fall short in evaluating cross-modal reasoning and complex question answering, which are essential for capturing complex interactions between narrative information and temporal patterns.
• To bridge this gap, we introduce Multimodal Time Series Benchmark (MTBench), a large-scale benchmark designed to evaluate large language models (LLMs) on time series and text understanding across financial and weather domains.
• MTbench comprises paired time series and textual data, including financial news with corresponding stock price movements and weather reports aligned with historical temperature records.