Summary of Multirc: Joint Learning For Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast, by Shiyan Hu et al.
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast
by Shiyan Hu, Kai Zhao, Xiangfei Qiu, Yang Shu, Jilin Hu, Bin Yang, Chenjuan Guo
First submitted to arxiv on: 21 Oct 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 A novel approach to unsupervised time series anomaly detection is proposed, focusing on predicting future anomalies rather than just detecting them. The method, called MultiRC, integrates reconstructive and contrastive learning for joint learning of anomaly prediction and detection. This includes a multi-scale structure and adaptive dominant period mask to account for diverse reaction times. Additionally, negative samples are generated to provide training momentum and prevent model degradation. The approach is evaluated on seven benchmark datasets from various fields, outperforming existing state-of-the-art methods in both anomaly prediction and detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive models can help identify anomalies in time series data without being told what’s normal or abnormal. This paper presents a new way to do this, called MultiRC. It combines two learning techniques: reconstructive learning helps the model learn patterns, while contrastive learning helps it recognize unusual events. The method also includes special tools to handle different reaction times and generate training examples that help prevent the model from getting worse over time. The approach was tested on many datasets and performed better than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Mask » Time series » Unsupervised
