k-Parameter Approach for False In-Season Anomaly Suppression in Daily
  Time Series Anomaly Detection
Vincent Yuansang Zha Vaishnavi Kommaraju Okenna Obi-Njoku Vijay Dakshinamoorthy Anirudh Agnihotri Nantes Kirsten
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.