GGLabYale/MTBench_finance_stock

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.

🏦 Stock Time-series

We provide high-resolution time series data for 2,993 U.S. stocks spanning a 10-year period (2013–2023). The data is recorded at 5-minute intervals, offering fine-grained temporal resolution for modeling and analysis.

Each stock record includes the following attributes:

• —Open, High, Low, Close prices (OHLC)
• —Volume of shares traded
• —VWAP (Volume-Weighted Average Price)
• —Number of Transactions

This dataset enables detailed financial forecasting, event correlation, and temporal pattern analysis.

📦 Other MTBench Datasets

🔹 Finance Domain

• —`MTBench_finance_news` 20,000 articles with URL, timestamp, context, and labels

• —`MTBench_finance_stock` Time series of 2,993 stocks (2013–2023)

• —`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` 2,000 news–series 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

🔹 Weather Domain

• —`MTBench_weather_news` Regional weather event descriptions

• —`MTBench_weather_temperature` Meteorological time series from 50 U.S. stations

• —`MTBench_weather_aligned_pairs_short` Short-range aligned weather text–series pairs

• —`MTBench_weather_aligned_pairs_long` Long-range aligned weather text–series pairs

• —`MTBench_weather_QA_short` Short-horizon QA with aligned weather data

• —`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:

@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}
}