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arXiv · 2311.08422

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
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Abstract

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.

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Vincent Yuansang Zha, Vaishnavi Kommaraju, Okenna Obi-Njoku, Vijay Dakshinamoorthy, Anirudh Agnihotri, Nantes Kirsten. 2023-11-10. k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection. https://arxiv.org/abs/2311.08422

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