---
license: apache-2.0
task_categories:
- time-series-forecasting
tags:
- timeseries
- forecasting
- benchmark
- gifteval
size_categories:
- 100K<n<1M

---

## GIFT-Eval
<!-- Provide a quick summary of the dataset. -->
![gift eval main figure](gifteval.png)

We present GIFT-Eval, a benchmark designed to advance zero-shot time series forecasting by facilitating evaluation across diverse datasets. GIFT-Eval includes 23 datasets covering 144,000 time series and 177 million data points, with data spanning seven domains, 10 frequencies, and a range of forecast lengths. This benchmark aims to set a new standard, guiding future innovations in time series foundation models.

To facilitate the effective pretraining and evaluation of foundation models, we also provide a non-leaking pretraining dataset --> [GiftEvalPretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain).

[📄 Paper](https://arxiv.org/abs/2410.10393)

[🖥️ Code](https://github.com/SalesforceAIResearch/gift-eval)

[📔 Blog Post]()

[🏎️ Leader Board](https://huggingface.co/spaces/Salesforce/GIFT-Eval)

## Submitting your results

If you want to submit your own results to our leaderborad please follow the instructions detailed in our [github repository](https://github.com/SalesforceAIResearch/gift-eval)

## Citation

If you find this benchmark useful, please consider citing:

```
@article{aksu2024giftevalbenchmarkgeneraltime,
      title={GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation}, 
      author={Taha Aksu and Gerald Woo and Juncheng Liu and Xu Liu and Chenghao Liu and Silvio Savarese and Caiming Xiong and Doyen Sahoo},
      journal = {arxiv preprint arxiv:2410.10393},
      year={2024},
}
```