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Detecting anomalies in a daily time series with a weekly pattern is a commontask with a wide range of applications. A typical way of performing the task isby using decomposition method. However, the method often generates falsepositive results where a data point falls within its weekly range but is justoff from its weekday position. We refer to this type of anomalies as "in-seasonanomalies", and propose a k-parameter approach to address the issue. Theapproach provides configurable extra tolerance for in-season anomalies tosuppress misleading alerts while preserving real positives. It yields favorableresult.

k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection