Buch ; Online: PatchAD
Patch-based MLP-Mixer for Time Series Anomaly Detection
2024
Abstract: Anomaly detection stands as a crucial aspect of time series analysis, aiming to identify abnormal events in time series samples. The central challenge of this task lies in effectively learning the representations of normal and abnormal patterns in a ... ...
Abstract | Anomaly detection stands as a crucial aspect of time series analysis, aiming to identify abnormal events in time series samples. The central challenge of this task lies in effectively learning the representations of normal and abnormal patterns in a label-lacking scenario. Previous research mostly relied on reconstruction-based approaches, restricting the representational abilities of the models. In addition, most of the current deep learning-based methods are not lightweight enough, which prompts us to design a more efficient framework for anomaly detection. In this study, we introduce PatchAD, a novel multi-scale patch-based MLP-Mixer architecture that leverages contrastive learning for representational extraction and anomaly detection. Specifically, PatchAD is composed of four distinct MLP Mixers, exclusively utilizing the MLP architecture for high efficiency and lightweight architecture. Additionally, we also innovatively crafted a dual project constraint module to mitigate potential model degradation. Comprehensive experiments demonstrate that PatchAD achieves state-of-the-art results across multiple real-world multivariate time series datasets. Our code is publicly available.\footnote{\url{https://github.com/EmorZz1G/PatchAD}} Comment: 13 pages, 16 figures, IJCAI 2024 under review, paper id 3166 |
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Schlagwörter | Computer Science - Machine Learning |
Thema/Rubrik (Code) | 006 |
Erscheinungsdatum | 2024-01-18 |
Erscheinungsland | us |
Dokumenttyp | Buch ; Online |
Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
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