README.md

---
license: apache-2.0
task_categories:
- time-series-forecasting
tags:
- time-series
- time-series-forecasting
- multimodality
- multimodal-time-series
- context-guided-time-series-forecasting
size_categories:
- 10K<n<100K
---

# CGTSF: Context-Guided Time Series Forecasting

## ✨ Introduction

The context-guided time series forecasting task entails the transformation of text into time series data. Relevant multimodal datasets are limited. To address these data gaps, we have collected three multimodal datasets that offer valuable resources for future research. The following table summarizes the statistics of these datasets. MSPG comprises 13 months of solar power generation data on 27 photovoltaic sites in Melbourne from 2021 to 2022. LEU encompasses 24 months of electricity usage data on 16 households in London from 2012 to 2013. PTF includes 12 months of traffic flow data on 32 traffic detectors in Paris during 2012. We gather raw time series records from [Kaggle](https://www.kaggle.com/), a prominent open-source platform. To prevent future data leakage, we incorporate only background, weather, and date as textual auxiliary information. The background includes a description of the dataset and its collection granularity. Weather encompasses forecast data obtained from [Open-Meteo](https://open-meteo.com/), including weather codes, temperatures, and sunrise and sunset times. Regarding dates, we include the raw date, day of the week, and holiday information. All auxiliary data is concatenated into coherent text and strictly aligned with the time series records by day.

| Dataset | Length | Frequency  |  Information   |
| :-----: | :----: | :--------: | :------------: |
|  MSPG   | 38016  | 15 Minutes |     Energy     |
|   LEU   | 35088  | 30 Minutes |     Energy     |
|   PTF   |  8760  |   1 Hour   | Transportation |

For details on CGTSF dataset, please refer to the [arXiv](https://arxiv.org/abs/2412.11376).

## 📝 Citation

If you find this repo or our work useful for your research, please consider citing the paper:

```tex
@inproceedings{
  author    = {Chengsen Wang and Qi Qi and Jingyu Wang and Haifeng Sun and Zirui Zhuang and Jinming Wu and Lei Zhang and Jianxin Liao},
  title     = {ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
}
```

## 📪 Contact

If you have any question, please contact [[email protected]]().