Summary of Predbench: Benchmarking Spatio-temporal Prediction Across Diverse Disciplines, by Zidong Wang and Zeyu Lu and Di Huang and Tong He and Xihui Liu and Wanli Ouyang and Lei Bai
PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines
by ZiDong Wang, Zeyu Lu, Di Huang, Tong He, Xihui Liu, Wanli Ouyang, Lei Bai
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper introduces PredBench, a benchmark for evaluating spatio-temporal prediction networks. Despite advances in the field, there is a lack of standardized evaluation frameworks. PredBench addresses this gap by conducting large-scale experiments with 12 methods and 15 datasets across various domains. The benchmark provides comprehensive evaluations with multi-dimensional metrics, enabling fair comparisons and insights into model capabilities. Our findings offer strategic directions for future developments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new tool called PredBench to help scientists compare different ways of predicting things that change over time and space. Right now, there’s no standard way to do this, so it can be hard to know which method is best. PredBench fixes this by testing 12 different methods with 15 different types of data from many fields. This helps us understand what each method can do and how they compare. |
