Summary of Joint Selective State Space Model and Detrending For Robust Time Series Anomaly Detection, by Junqi Chen et al.
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection
by Junqi Chen, Xu Tan, Sylwan Rahardja, Jiawei Yang, Susanto Rahardja
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper proposes an anomaly detector for Time Series Anomaly Detection (TSAD) tasks, leveraging a selective state space model to capture long-term dependencies and improve generalization in non-stationary data. The approach addresses two key challenges: modeling long-range dependency and generalization in the presence of trend components. Experiments on real-world datasets show that the proposed methods outperform 12 baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to detect unusual patterns in time series data, like stock prices or weather forecasts. It uses a special kind of model called a selective state space model, which is good at finding long-term relationships between different parts of the data. The approach also gets rid of big trends that can make it hard for models to work well on new, unseen data. By doing this, the paper shows how its method performs better than other approaches. |
Keywords
* Artificial intelligence * Anomaly detection * Generalization * Time series
