| --- |
| dataset_info: |
| features: |
| - name: input_timestamps |
| sequence: float64 |
| - name: input_window |
| sequence: float64 |
| - name: output_timestamps |
| sequence: float64 |
| - name: output_window |
| sequence: float64 |
| - name: text |
| dtype: string |
| - name: trend |
| dtype: string |
| - name: technical |
| dtype: string |
| - name: alignment |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 40760650 |
| num_examples: 525 |
| download_size: 22910094 |
| dataset_size: 40760650 |
| 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. |
|
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| 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 and News Pair |
|
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| This dataset contains aligned pairs of financial news articles and corresponding stock time-series data, designed to evaluate models on **event-driven financial reasoning** and **news-aware forecasting**. |
|
|
| ### Pairing Process |
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| Each pair is formed by matching a news article’s **publication timestamp** with a relevant stock’s **time-series window** surrounding the event |
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| To assess the impact of the news, we compute the **average percentage price change** across input/output windows and label directional trends (e.g., `+2% ~ +4%`). A **semantic analysis** of the article is used to annotate the sentiment and topic, allowing us to compare narrative signals with actual market movement. |
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| We observed that not all financial news accurately predicts future price direction. To quantify this, we annotate **alignment quality**, indicating whether the sentiment in the article **aligns with observed price trends**. Approximately **80% of the pairs** in the dataset show consistent alignment between news sentiment and trend direction. |
|
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|
|
| ### Each pair includes: |
|
|
| - `"input_timestamps"` / `"output_timestamps"`: Aligned time ranges (5-minute resolution) |
| - `"input_window"` / `"output_window"`: Time-series data (OHLC, volume, VWAP, transactions) |
| - `"text"`: Article metadata |
| - `content`, `timestamp_ms`, `published_utc`, `article_url` |
| - Annotated `label_type`, `label_time`, `label_sentiment` |
| - `"trend"`: Ground truth price trend and bin labels |
| - Percentage changes and directional bins (e.g., `"-2% ~ +2%"`) |
| - `"technical"`: Computed technical indicators |
| - SMA, EMA, MACD, Bollinger Bands (for input, output, and overall windows) |
| - `"alignment"`: Label indicating semantic-trend consistency (e.g., `"consistent"`) |
|
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|
|
|
|
| ## 📦 Other MTBench Datasets |
|
|
| ### 🔹 Finance Domain |
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| - [`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 |
|
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|
|
|
|
| ## 🧠 Supported Tasks |
|
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| MTBench supports a wide range of multimodal and temporal reasoning tasks, including: |
|
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| - 📈 **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** |
|
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|
|
|
|
| ## 📄 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} |
| } |
| |
