MMTS-BENCH: A Comprehensive Benchmark for Time Series Understanding and Reasoning

TL;DR

MMTS-BENCH is a comprehensive benchmark designed to evaluate large language models on diverse time series understanding and reasoning tasks, highlighting current limitations and guiding future improvements.

Contribution

Introduces MMTS-BENCH, a hierarchical, multimodal benchmark with 2,424 QA pairs for assessing LLMs on time series tasks, and provides extensive evaluation insights.

Findings

TS-LLMs lag behind general-purpose LLMs in cross-domain tasks.

LLMs perform worse on local than global time series tasks.

Chain-of-thought reasoning and multimodal integration improve performance.

Abstract

Time series data are central to domains such as finance, healthcare, and cloud computing, yet existing benchmarks for evaluating various large language models (LLMs) on temporal tasks remain scattered and unsystematic. To bridge this gap, we introduce MMTS-BENCH, a comprehensive multimodal benchmark built upon a hierarchical taxonomy of time-series tasks, spanning structural awareness, feature analysis, temporal reasoning, sequence matching and cross-modal alignment. MMTS-BENCH comprises 2,424 time series question answering (TSQA) pairs across 4 subsets: Base, InWild, Match, and Align, generated through a progressive real-world QA framework and modular synthetic data construction. We conduct extensive evaluations on closed-source, open-source LLMs and existing time series adapted large language models (TS-LLMs), revealing that: (1) TS-LLMs significantly lag behind general-purpose LLMs in cross-domain generalization, (2) LLMs show weaknesses in local tasks compared to global tasks, (3) chain-of-thought (CoT) reasoning and multimodal integration substantially improve performance, and (4) the dominant factor in existing TS-LLMs remains the backbone network capability rather than the time series encoder design. MMTS-BENCH not only provides a rigorous evaluation framework but also offers clear directions for advancing LLMs toward robust, interpretable, and generalizable time-series reasoning.