Summary of Rethinking the Power Of Timestamps For Robust Time Series Forecasting: a Global-local Fusion Perspective, by Chengsen Wang et al.
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
by Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun, Zirui Zhuang, Jinming Wu, Jianxin Liao
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel framework for time series forecasting, called GLAFF, is proposed to address limitations in existing methods. Current approaches primarily focus on local observations and underutilize timestamps as a source of global information. The absence of this information can lead to reduced robustness in prediction capabilities when dealing with real-world data that may be polluted. To overcome these issues, GLAFF models individual timestamps to capture global dependencies, allowing for adaptive adjustment of combined weights for both global and local information. This plugin architecture enables seamless collaboration with any time series forecasting backbone. Extensive experiments on nine real-world datasets demonstrate that GLAFF significantly enhances the average performance of mainstream forecasting models by 12.5%, surpassing the previous state-of-the-art method by 5.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to improve time series forecasting is being developed. Right now, most methods focus on local data and don’t use timestamps (the dates) very well. This can make it harder for algorithms to predict things correctly when dealing with real-world data that might be messy. To fix this problem, scientists are creating a new framework called GLAFF. It takes the individual timestamps and uses them to capture bigger patterns in the data. Then, it adjusts how much weight is given to both local and global information. This lets GLAFF work well with any forecasting method. In tests on real-world data, GLAFF did better than other methods by 12.5%. |
Keywords
» Artificial intelligence » Time series
