DOI : https://doi.org/10.18517/ijaseit.14.6.20451

Comprehensive Analysis and Improved Techniques for Anomaly Detection in Time Series Data with Autoencoder Models

Sarvarbek Erniyazov (1), Yong-Min Kim (2), Mohammed Abdul Jaleel (3), Chang Gyoon Lim (4)
(1) Department of Computer Engineering, Chonnam National University, Yeosu, Jeonnam, Republic of Korea
(2) Department of Electronic Commerce, Chonnam National University, Yeosu, Jeonnam, Republic of Korea
(3) Department of Applied Informatics, Kimyo International University in Tashkent, Tashkent, Uzbekistan
(4) Department of Computer Engineering, Chonnam National University, Yeosu, Jeonnam, Republic of Korea
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How to cite (IJASEIT) :
Erniyazov, Sarvarbek, et al. “Comprehensive Analysis and Improved Techniques for Anomaly Detection in Time Series Data With Autoencoder Models”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 6, Dec. 2024, pp. 1861-7, doi:10.18517/ijaseit.14.6.20451.
Citation Format :
Anomaly detection is critical in various sectors, offering significant advantages by precisely identifying and mitigating system failures and errors, thus preventing severe losses. This study provides a comprehensive comparative anomaly detection analysis through two sophisticated deep learning models: Autoencoder and Long Short-Term Memory (LSTM) Autoencoder, explicitly focusing on temperature and sound time series data. The paper starts with a detailed theoretical foundation, elaborating on both models' mechanics and mathematical formulations. We then advance to the empirical phase, where these models are rigorously trained and tested against a robust dataset. The effectiveness of each model is meticulously assessed through a suite of metrics that gauge their accuracy, sensitivity, and robustness in anomaly detection scenarios. Additionally, we explore the deployment of these models in a real-time environment, where they actively engage in anomaly detection on incoming data streams. The anomalies detected are dynamically displayed on a user-friendly graphical interface, making the results readily accessible and interpretable for users at all levels of technical expertise. Quantitative evaluations of the models are conducted using key performance metrics such as accuracy, precision, recall, and F1-score. Our analysis reveals that the LSTM Autoencoder model excels with an impressive accuracy rate of 99%, while other metrics also affirm its superior performance, marking it as exceptionally effective and reliable. This study highlights the LSTM Autoencoder's advanced anomaly detection capabilities and establishes its superiority over the traditional Autoencoder model in processing complex time series data. The insights gained here are crucial for industries focused on predictive maintenance and quality control, where early anomaly detection is key to maintaining operational efficiency and safety.

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