Learning Local Patterns of Time Series for Anomaly Detection †
Abstract
:1. Introduction
2. Related Work
3. Preliminaries
3.1. Notation
3.1.1. Time Series Data
3.1.2. Time Series Subsequence
3.2. LOPAS Transform
3.3. Assignment Factor
4. Proposed Method
4.1. Basic Concept
4.2. Learning Method
- Assign subsequence to clusters by LOPAS transformation.
- Obtain a Gaussian model for each cluster.
Algorithm 1 Algorithm of the proposed method |
|
4.3. Evaluation Method
5. Experiment and Evaluation
5.1. Accuracy in the UCR Dataset
5.2. Visualization Evaluation Using Current Data
5.3. Limitations of Our Method
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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LOPAD (Proposed) | OCLTS | |
---|---|---|
Plane | 1.000 | 1.000 |
Trace | 0.985 | 1.000 |
SonyAIBORobotSurface1 | 0.992 | 0.950 |
SonyAIBORobotSurface2 | 0.948 | 0.914 |
ECGFivedays | 1.000 | 0.980 |
ECG200 | 0.801 | 0.834 |
ECG5000 | 0.932 | 0.984 |
MiddlePhalanxTW | 0.994 | 0.991 |
ProximalPhalanxOutlineAgeGroup | 0.899 | 0.883 |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Kotera, K.; Yamaguchi, A.; Ueno, K. Learning Local Patterns of Time Series for Anomaly Detection. Eng. Proc. 2023, 39, 82. https://doi.org/10.3390/engproc2023039082
Kotera K, Yamaguchi A, Ueno K. Learning Local Patterns of Time Series for Anomaly Detection. Engineering Proceedings. 2023; 39(1):82. https://doi.org/10.3390/engproc2023039082
Chicago/Turabian StyleKotera, Kento, Akihiro Yamaguchi, and Ken Ueno. 2023. "Learning Local Patterns of Time Series for Anomaly Detection" Engineering Proceedings 39, no. 1: 82. https://doi.org/10.3390/engproc2023039082