afeng/MTBench_finance_aligned_pairs_short

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 and News Pair

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

Each pair is formed by matching a news article’s publication timestamp with a relevant stock’s time-series window surrounding the event

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.

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.

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")

📦 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}
}