Summary of Deconfounding Time Series Forecasting, by Wentao Gao et al.
Deconfounding Time Series Forecasting
by Wentao Gao, Feiyu Yang, Mengze Hong, Xiaojing Du, Zechen Hu, Xiongren Chen, Ziqi Xu
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes an enhanced forecasting approach for time series forecasting, addressing the issue of latent confounders in predictive models. The method incorporates representations of unobserved variables derived from historical data to improve the accuracy and robustness of forecasts. This is demonstrated through its application to climate science data, showing significant improvements over traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to address the challenge of predicting future outcomes while considering the influence of latent confounders that affect both predictors and target outcomes. The proposed approach is designed to improve time series forecasting by integrating representations of these confounders into the predictive process. The results show that this method can lead to more accurate and robust predictions. |
Keywords
» Artificial intelligence » Time series
