Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908300032868352 |
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| author | Golchin, Bahareh Rekabdar, Banafsheh |
| author_facet | Golchin, Bahareh Rekabdar, Banafsheh |
| contents | A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods often struggle with manual parameter tuning and cannot adapt to new anomaly types. Our method overcomes these limitations by integrating Deep Reinforcement Learning (DRL) with a Variational Autoencoder (VAE) and Active Learning. By incorporating a Long Short-Term Memory (LSTM) network, our approach models sequential data and its dependencies effectively, allowing for the detection of new anomaly classes with minimal labeled data. Our innovative DRL- VAE and Active Learning combination significantly improves existing methods, as shown by our evaluations on real-world datasets, enhancing anomaly detection techniques and advancing time series analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning Golchin, Bahareh Rekabdar, Banafsheh Machine Learning A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods often struggle with manual parameter tuning and cannot adapt to new anomaly types. Our method overcomes these limitations by integrating Deep Reinforcement Learning (DRL) with a Variational Autoencoder (VAE) and Active Learning. By incorporating a Long Short-Term Memory (LSTM) network, our approach models sequential data and its dependencies effectively, allowing for the detection of new anomaly classes with minimal labeled data. Our innovative DRL- VAE and Active Learning combination significantly improves existing methods, as shown by our evaluations on real-world datasets, enhancing anomaly detection techniques and advancing time series analysis. |
| title | Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.02999 |