2026-07-21T14:14:05Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:21224 2024-10-22T05:39:58Z ddc:004 open_access

Time Series Anomaly Detection Leveraging MSE Feedback with AutoEncoder and RNN Delibasoglu, Ibrahim Heintz, Fredrik Time series Anomaly Neural networks 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 Schloss Dagstuhl – Leibniz-Zentrum für Informatik Ibrahim Delibasoglu and Fredrik Heintz 2024 Is Part Of LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.TIME.2024.17 urn:nbn:de:0030-drops-212244 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.17 eng https://creativecommons.org/licenses/by/4.0/legalcode