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Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN

Authors Ibrahim Delibasoglu , Fredrik Heintz

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Ibrahim Delibasoglu
  • Department of Computer and Information Science (IDA), Linköping University, Sweden
  • Software Engineering, Sakarya University, Turkey
Fredrik Heintz
  • Department of Computer and Information Science (IDA), Linköping University, Sweden

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Ibrahim Delibasoglu and Fredrik Heintz. Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 17:1-17:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.TIME.2024.17

Abstract

Anomaly detection in time series data is a critical task in various domains, including finance, healthcare, cybersecurity and industry. Traditional methods, such as time series decomposition, clustering, and density estimation, have provided robust solutions for identifying anomalies that exhibit distinct patterns or significant deviations from normal data distributions. Recent advancements in machine learning and deep learning have further enhanced these capabilities. This paper introduces a novel method for anomaly detection that combines the strengths of autoencoders and recurrent neural networks (RNNs) with an reconstruction error feedback mechanism based on Mean Squared Error. We compare our method against classical techniques and recent approaches like OmniAnomaly, which leverages stochastic recurrent neural networks, and the Anomaly Transformer, which introduces association discrepancy to capture long-range dependencies and DCDetector using contrastive representation learning with multi-scale dual attention. Experimental results demonstrate that our method achieves superior overall performance in terms of precision, recall, and F1 score. The source code is available at http://github.com/mribrahim/AE-FAR

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ACM Subject Classification
  • Computing methodologies → Machine learning algorithms
Keywords
  • Time series
  • Anomaly
  • Neural networks

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References

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