--- dataset_info: features: - name: DATE sequence: string - name: temperature sequence: float32 - name: dew_point_temperature sequence: float32 - name: relative_humidity sequence: float32 - name: station_level_pressure sequence: float32 - name: sea_level_pressure sequence: float32 - name: wind_speed sequence: float32 - name: wind_direction sequence: float32 - name: visibility sequence: float32 - name: altimeter sequence: float32 - name: precipitation_3_hour sequence: float32 - name: precipitation_6_hour sequence: float32 - name: precipitation_9_hour sequence: float32 - name: precipitation_12_hour sequence: float32 - name: precipitation_15_hour sequence: float32 - name: precipitation_18_hour sequence: float32 - name: precipitation_21_hour sequence: float32 - name: precipitation_24_hour sequence: float32 splits: - name: train num_bytes: 1173537799 num_examples: 49 download_size: 168154556 dataset_size: 1173537799 configs: - config_name: default data_files: - split: train path: data/train-* --- # MTBench: A Multimodal Time Series Benchmark **MTBench** ([Huggingface](https://huggingface.co/collections/afeng/mtbench-682577471b93095c0613bbaa), [Github](https://github.com/Graph-and-Geometric-Learning/MTBench), [Arxiv](https://arxiv.org/pdf/2503.16858)) 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. ## 📦 MTBench Datasets ### 🔹 Finance Domain - [`MTBench_finance_news`](https://huggingface.co/datasets/afeng/MTBench_finance_news) 20,000 articles with URL, timestamp, context, and labels - [`MTBench_finance_stock`](https://huggingface.co/datasets/afeng/MTBench_finance_stock) Time series of 2,993 stocks (2013–2023) - [`MTBench_finance_aligned_pairs_short`](https://huggingface.co/datasets/afeng/MTBench_finance_aligned_pairs_short) 2,000 news–series pairs - Input: 7 days @ 5-min - Output: 1 day @ 5-min - [`MTBench_finance_aligned_pairs_long`](https://huggingface.co/datasets/afeng/MTBench_finance_aligned_pairs_long) 2,000 news–series pairs - Input: 30 days @ 1-hour - Output: 7 days @ 1-hour - [`MTBench_finance_QA_short`](https://huggingface.co/datasets/afeng/MTBench_finance_QA_short) 490 multiple-choice QA pairs - Input: 7 days @ 5-min - Output: 1 day @ 5-min - [`MTBench_finance_QA_long`](https://huggingface.co/datasets/afeng/MTBench_finance_QA_long) 490 multiple-choice QA pairs - Input: 30 days @ 1-hour - Output: 7 days @ 1-hour ### 🔹 Weather Domain - [`MTBench_weather_news`](https://huggingface.co/datasets/afeng/MTBench_weather_news) Regional weather event descriptions - [`MTBench_weather_temperature`](https://huggingface.co/datasets/afeng/MTBench_weather_temperature) Meteorological time series from 50 U.S. stations - [`MTBench_weather_aligned_pairs_short`](https://huggingface.co/datasets/afeng/MTBench_weather_aligned_pairs_short) Short-range aligned weather text–series pairs - [`MTBench_weather_aligned_pairs_long`](https://huggingface.co/datasets/afeng/MTBench_weather_aligned_pairs_long) Long-range aligned weather text–series pairs - [`MTBench_weather_QA_short`](https://huggingface.co/datasets/afeng/MTBench_weather_QA_short) Short-horizon QA with aligned weather data - [`MTBench_weather_QA_long`](https://huggingface.co/datasets/afeng/MTBench_weather_QA_long) Long-horizon QA for temporal and contextual reasoning ## 🧠 Supported Tasks MTBench supports a wide range of multimodal and temporal reasoning tasks, including: - 📈 **News-aware time series forecasting** - 📊 **Event-driven trend analysis** - ❓ **Multimodal question answering (QA)** - 🔄 **Text-to-series correlation analysis** - 🧩 **Causal inference in financial and meteorological systems** ## 📄 Citation If you use MTBench in your work, please cite: ```bibtex @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} }