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README.md

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+---
+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. -->
+
+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.
+
+[📄 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},
+}
+```