2023/11/10 by Vincent Yuansang Zha, Vaishnavi Kommaraju, Zha, Vincent Yuansang +9
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2311.08422
openalex publication_date 2023/11/10 · openalex created_date 2023/11/17 · openalex updated_date 2026/07/28
Detecting anomalies in a daily time series with a weekly pattern is a common task with a wide range of applications. A typical way of performing the task is by using decomposition method. However, the method often generates false positive results where a data point falls within its weekly range but is just off from its weekday position. We refer to this type of anomalies as "in-season anomalies", and propose a k-parameter approach to address the issue. The approach provides configurable extra tolerance for in-season anomalies to suppress misleading alerts while preserving real positives. It yields favorable result.