Summary of Robusttsf: Towards Theory and Design Of Robust Time Series Forecasting with Anomalies, by Hao Cheng et al.
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
by Hao Cheng, Qingsong Wen, Yang Liu, Liang Sun
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of time series forecasting in the presence of anomalies. They demonstrate that existing methods assume clean data and are thus inferior when dealing with contaminated datasets. The authors define three types of anomalies statistically, analyze their impact on loss robustness and sample robustness, and propose a simple algorithm to learn a robust forecasting model. Experiments show that this approach outperforms existing methods in terms of robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for many real-world applications, but current techniques assume clean data without anomalies. However, in practice, data can be contaminated with different types of anomalies. To address this issue, researchers statistically define three types of anomalies and analyze how they affect the loss and sample robustness of forecasting models. They propose a simple algorithm to learn a robust model that outperforms existing approaches. |
Keywords
* Artificial intelligence * Time series
