Datasets:
metadata
license: apache-2.0
task_categories:
- time-series-forecasting
tags:
- timeseries
- forecasting
- benchmark
- gifteval
size_categories:
- 100K<n<1M
GIFT-Eval
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.
Submitting your results
If you want to submit your own results to our leaderborad please follow the instructions detailed in our github repository
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},
}