2018/11/30 by Sooyeon Lee, Huy Kang Kim, Lee, Sooyeon +1
Computer Science · Economics, Econometrics and Finance · Mathematics · #Anomaly Detection Techniques and Applications #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Time Series Analysis and Forecasting #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1811.12634
6 pages, 4 figures, In Proceedings of the 19th World Conference on Information Security and Applications (WISA) 2018
arxiv created 2018/11/30 · openalex publication_date 2018/11/30 · arxiv updated 2018/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal Trend decomposition using Loess (STL), to detect complex and various anomalies. Usually, SARIMA and STL are used only for stationary and periodic time-series, but by combining, we show they can detect anomalies with high accuracy for data that is even noisy and non-periodic. We compared the algorithm to Long Short Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection system. We used a total of seven real-world datasets and four artificial datasets with different time-series properties to verify the performance of the proposed algorithm.