Summary of Tfb: Towards Comprehensive and Fair Benchmarking Of Time Series Forecasting Methods, by Xiangfei Qiu et al.
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
by Xiangfei Qiu, Jilin Hu, Lekui Zhou, Xingjian Wu, Junyang Du, Buang Zhang, Chenjuan Guo, Aoying Zhou, Christian S. Jensen, Zhenli Sheng, Bin Yang
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 The proposed Time Series Forecasting (TSF) method, TFB, is an automated benchmark designed to comprehensively evaluate and compare various TSF methods. To address limitations in existing benchmarks, TFB includes datasets from 10 different domains, provides a time series characterization to ensure comprehensive coverage, and supports a diverse range of evaluation strategies and metrics. The benchmark features a flexible and scalable pipeline that eliminates biases, allowing for fair comparisons between methods. In this study, TFB is employed to evaluate 21 univariate TSF methods on 8,068 datasets and 14 multivariate TSF methods on 25 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TFB is a new way to compare different forecasting methods. It uses data from many different domains like traffic, energy, and health. This helps ensure that the forecasters are being tested fairly and that we get a good idea of which method works best for each type of data. The benchmark also lets us test different types of forecasters, like statistical learning, machine learning, and deep learning. By using this benchmark, researchers can compare their methods more easily and find the best one for each problem. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Time series
