نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشکده ریاضی و علوم کامپیوتر، دانشگاه دامغان، دامغان، ایران
2 دانشکده مهندسی، دانشگاه دامغان، دامغان، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Anomaly detection in multivariate time series has been an active research area due to its widespread application in various fields. Window-based methods are popular in the anomaly detection domain. These methods identify anomalous windows rather than specific anomalous points, even if not all points within the window are anomalies. It is a critical limitation of window-based methods. We propose an unsupervised sliding window-based model for detecting anomalies in multivariate time series to address this limitation. Our model employs a sliding mechanism to iterate through the input time series multiple times and utilizes a consensus function to aggregate different window anomaly scores. This mechanism facilitates the discovery of more anomalous subsequences, even if they are not precisely confined within a specific window. To evaluate the performance of the proposed method, several experiments on synthetic and real-world datasets, including SKAB and MSL, with multiple indices. The results confirm the superiority of the proposed method. The method achieves an Fscore of 0.902 for SKAB and 0.620 for MSL, which are twice as good as the results achieved by other methods.
کلیدواژهها [English]
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