Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation

Understand

Understanding time series data is fundamental to many real-world applications.

• Recent work explores multimodal large language models (MLLMs) to enhance time series understanding with contextual information beyond numerical signals.
• This area has grown from 7 papers in 2023 to over 580 in 2025, yet existing methods struggle in real-world settings.
• We analyze 20 influential works from 2025 across model design, task formulation, and evaluation, and identify critical gaps: methods adapt NLP techniques with limited attention to core time series properties; tasks remain restricted to traditional prediction and classification; and evaluations emphasize benchmarks over robustness, interpretability, or decision relevance.