Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks
In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-sta...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10966923/ |
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| author | Ayan Chatterjee Vajira Thambawita Michael A. Riegler Pal Halvorsen |
| author_facet | Ayan Chatterjee Vajira Thambawita Michael A. Riegler Pal Halvorsen |
| author_sort | Ayan Chatterjee |
| collection | DOAJ |
| description | In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets. |
| format | Article |
| id | doaj-art-2b9eb3f3054e4688a9866ddefef3088e |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-2b9eb3f3054e4688a9866ddefef3088e2025-08-20T02:11:25ZengIEEEIEEE Access2169-35362025-01-0113709807100610.1109/ACCESS.2025.356137510966923Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese NetworksAyan Chatterjee0https://orcid.org/0000-0003-0407-7702Vajira Thambawita1https://orcid.org/0000-0001-6026-0929Michael A. Riegler2https://orcid.org/0000-0002-3153-2064Pal Halvorsen3https://orcid.org/0000-0003-2073-7029Department of Digital Technology, STIFTELSEN NILU, Kjeller, NorwayDepartment of Holistic Systems, Simula Metropolitan Center for Digital Engineering (SimulaMet), Oslo, NorwayDepartment of Information Technology, Oslo Metropolitan University, Oslo, NorwayDepartment of Information Technology, Oslo Metropolitan University, Oslo, NorwayIn time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets.https://ieeexplore.ieee.org/document/10966923/Univariate time-seriesanomaly detectionsiamese neural networks1D CNNsquantile smoothingpredictive modeling |
| spellingShingle | Ayan Chatterjee Vajira Thambawita Michael A. Riegler Pal Halvorsen Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks IEEE Access Univariate time-series anomaly detection siamese neural networks 1D CNNs quantile smoothing predictive modeling |
| title | Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks |
| title_full | Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks |
| title_fullStr | Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks |
| title_full_unstemmed | Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks |
| title_short | Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks |
| title_sort | supervised anomaly detection in univariate time series using 1d convolutional siamese networks |
| topic | Univariate time-series anomaly detection siamese neural networks 1D CNNs quantile smoothing predictive modeling |
| url | https://ieeexplore.ieee.org/document/10966923/ |
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