Summary of Tsa on Autopilot: Self-tuning Self-supervised Time Series Anomaly Detection, by Boje Deforce et al.
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection
by Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo, Leman Akoglu
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 approach to time series anomaly detection (TSAD), focusing on developing an unsupervised model that can detect various types of anomalies without labeled data. The authors highlight the challenge in selecting suitable data augmentations for self-supervised models, which is crucial for effective TSAD. They introduce TSAP, a differentiable augmentation architecture with an unsupervised validation loss to optimize augmentation hyperparameters end-to-end. Case studies demonstrate TSAP’s ability to select the optimal augmentation type and associated hyperparameters, outperforming established baselines on diverse TSAD tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TSAP is a new model that can detect anomalies in time series data without any labeled information. It uses special techniques to change the input data and create fake anomalies for training. The problem with this approach is choosing the right techniques to use, which can be difficult. The authors created TSAP to solve this problem by automatically adjusting the techniques used to create fake anomalies. This model performs better than other models on different types of anomaly detection tasks. |
Keywords
* Artificial intelligence * Anomaly detection * Self supervised * Time series * Unsupervised
