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

TL;DR

This paper introduces RPE, a robust, efficient online anomaly detection algorithm for univariate time-series that accurately identifies anomalies even with large corruptions, outperforming existing methods.

Contribution

The paper proposes a novel, closed-form, robust projection-based method for anomaly detection in time-series that handles multiple large anomalies within windows.

Findings

RPE outperforms existing methods in numerical experiments.

RPE can identify corrupted time-stamps accurately.

The algorithm is computationally efficient and suitable for data-scarce scenarios.

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.