Finder: A novel approach of change point detection for multivariate time series
| Autor: | Haizhou Du, Ziyi Duan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Computer science business.industry Deep learning Feature extraction Pattern recognition 02 engineering and technology Artificial Intelligence Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Time series business F1 score Change detection Transformer (machine learning model) |
| Zdroj: | Applied Intelligence. 52:2496-2509 |
| ISSN: | 1573-7497 0924-669X |
| DOI: | 10.1007/s10489-021-02532-x |
| Popis: | The multivariate time series often contain complex mixed inputs, with complex correlations between them. Detecting change points in multivariate time series is of great importance, which can find anomalies early and reduce losses, yet very challenging as it is affected by many complex factors, i.e., dynamic correlations and external factors. The performance of traditional methods typically scales poorly. In this paper, we propose Finder, a novel approach of change point detection via multivariate fusion attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ multi-level attention networks based on the Transformer and integrate the external factor fusion component, achieving feature extraction and fusion of multivariate data. Secondly, in the change point detection module, a deep learning classifier is used to detect change points, improving efficiency and accuracy. Extensive experiments prove the superiority and effectiveness of Finder on two real-world datasets. Our approach outperforms the state-of-the-art methods by up to 10.50% on the F1 score. |
| Databáze: | OpenAIRE |
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