Summary of Gift-eval: a Benchmark For General Time Series Forecasting Model Evaluation, by Taha Aksu et al.
GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
by Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, Doyen Sahoo
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A pioneering benchmark for evaluating time series foundation models, GIFT-Eval, is introduced, comprising 23 datasets across seven domains, covering diverse tasks and prediction lengths. The benchmark facilitates the pretraining and evaluation of foundation models, addressing a critical gap in advancing these models. By analyzing 17 baselines, including statistical models, deep learning models, and foundation models, insights are gained that can guide future developments in time series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series foundation models excel at zero-shot forecasting without explicit training. To promote evaluation across diverse datasets, the General Time Series Forecasting Model Evaluation (GIFT-Eval) is introduced. It includes 23 datasets covering 144,000 time series and 177 million data points from seven domains, with varying frequencies and prediction lengths. |
Keywords
» Artificial intelligence » Deep learning » Pretraining » Time series » Zero shot
