Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series

• URL: http://arxiv.org/abs/2205.15548v1
• Date: Tue, 31 May 2022 05:41:58 GMT
• Title: Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series
• Authors: Mostafa Rahmani, Anoop Deoras, Laurent Callot
• Abstract summary: The proposed method, dubbed RPE, is a window-based method. RPE is robust to the presence of anomalies in its window and it can distinguish the anomalies in time-stamp level. An extensive set of numerical experiments show that RPE can outperform the existing approaches with a notable margin.
• Score: 8.121462458089141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based method and in sharp contrast to the existing window-based methods, it is robust to the presence of anomalies in its window and it can distinguish the anomalies in time-stamp level. RPE leverages the linear structure of the trajectory matrix of the time-series and employs a robust projection step which makes the algorithm able to handle the presence of multiple arbitrarily large anomalies in its window. A closed-form/non-iterative algorithm for the robust projection step is provided and it is proved that it can identify the corrupted time-stamps. RPE is a great candidate for the applications where a large training data is not available which is the common scenario in the area of time-series. An extensive set of numerical experiments show that RPE can outperform the existing approaches with a notable margin.

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