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MTBench: A Multimodal Time Series Benchmark
MTBench (Huggingface, Github, Arxiv) is a suite of multimodal datasets for evaluating large language models (LLMs) in temporal and cross-modal reasoning tasks across finance and weather domains.
Each benchmark instance aligns high-resolution time series (e.g., stock prices, weather data) with textual context (e.g., news articles, QA prompts), enabling research into temporally grounded and multimodal understanding.
๐ฆ MTBench Datasets
๐น Finance Domain
MTBench_finance_news
20,000 articles with URL, timestamp, context, and labelsMTBench_finance_stock
Time series of 2,993 stocks (2013โ2023)MTBench_finance_aligned_pairs_short
2,000 newsโseries pairs- Input: 7 days @ 5-min
- Output: 1 day @ 5-min
MTBench_finance_aligned_pairs_long
2,000 newsโseries pairs- Input: 30 days @ 1-hour
- Output: 7 days @ 1-hour
MTBench_finance_QA_short
490 multiple-choice QA pairs- Input: 7 days @ 5-min
- Output: 1 day @ 5-min
MTBench_finance_QA_long
490 multiple-choice QA pairs- Input: 30 days @ 1-hour
- Output: 7 days @ 1-hour
๐น Weather Domain
MTBench_weather_news
Regional weather event descriptionsMTBench_weather_temperature
Meteorological time series from 50 U.S. stationsMTBench_weather_aligned_pairs_short
Short-range aligned weather textโseries pairsMTBench_weather_aligned_pairs_long
Long-range aligned weather textโseries pairsMTBench_weather_QA_short
Short-horizon QA with aligned weather dataMTBench_weather_QA_long
Long-horizon QA for temporal and contextual reasoning
๐ง Supported Tasks
MTBench supports a wide range of multimodal and temporal reasoning tasks, including:
- ๐ News-aware time series forecasting
- ๐ Event-driven trend analysis
- โ Multimodal question answering (QA)
- ๐ Text-to-series correlation analysis
- ๐งฉ Causal inference in financial and meteorological systems
๐ Citation
If you use MTBench in your work, please cite:
@article{chen2025mtbench,
title={MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering},
author={Chen, Jialin and Feng, Aosong and Zhao, Ziyu and Garza, Juan and Nurbek, Gaukhar and Qin, Cheng and Maatouk, Ali and Tassiulas, Leandros and Gao, Yifeng and Ying, Rex},
journal={arXiv preprint arXiv:2503.16858},
year={2025}
}
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