Summary of Tsinr: Capturing Temporal Continuity Via Implicit Neural Representations For Time Series Anomaly Detection, by Mengxuan Li et al.
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
by Mengxuan Li, Ke Liu, Hongyang Chen, Jiajun Bu, Hongwei Wang, Haishuai Wang
First submitted to arxiv on: 18 Nov 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 a novel method for time series anomaly detection, called TSINR (Time Series Implicit Neural Representation), which addresses the challenge of capturing normal patterns in the presence of unlabeled anomalous data points. The proposed approach uses implicit neural representation (INR) reconstruction to prioritize low-frequency signals and improve performance on discontinuous anomaly data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to find unusual patterns in time series data, called TSINR. It’s like trying to find the odd one out in a group of people. The method uses something called INR (Implicit Neural Representation) to learn about normal patterns and then looks for things that don’t fit. This helps it find the anomalies better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Time series
