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README.md

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+# 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.
+
+## 🏦 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`](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}
+}