k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection
Vincent Yuansang Zha, Vaishnavi Kommaraju, Okenna Obi‐Njoku, Dakshinamoorthy, Vijay, Anirudh Agnihotri, Nantes Kirsten · arXiv (Cornell University) · 2023
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.