Summary of Causal Discovery-driven Change Point Detection in Time Series, by Shanyun Gao et al.
Causal Discovery-Driven Change Point Detection in Time Series
by Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, Murat Kocaoglu
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 two-stage non-parametric algorithm is proposed for detecting abrupt changes in specific components of multivariate time series data. The approach first learns parts of the underlying structural causal model using constraint-based discovery methods, then employs conditional relative Pearson divergence estimation to identify change points. This method relaxes the typical assumption of independent and identically distributed (IID) samples, enabling focus on causal mechanisms and facilitating access to IID samples. The algorithm is evaluated on both synthetic and real-world datasets, demonstrating its correctness and utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to spot changes in certain parts of a set of time series data is developed. This method works by first figuring out the rules that govern how the data was generated, then looking for big differences between nearby segments. It’s useful when you only care about specific parts of the data and don’t want changes in other parts to affect your results. |
Keywords
* Artificial intelligence * Time series
