A combination of CUSUM-EWMA for Anomaly Detection in time series data

In this work we investigate the use of parametric statistical methods for Anomaly Detection in time series data. The approach involves the use of simple and computationally efficient algorithms, the Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA), that have demonstrated an acceptable performance in detecting different shifts from the process mean. However, while the performance of these algorithms is found to be adequate in datasets where anomalies have a profound form, they produce many false positives when anomalies become more complex. To address this limitation, we propose a solution that has greater flexibility, in the form of a combined CUSUM-EWMA algorithm. Four different statistical methods are investigated and implemented, including the classic CUSUM and EWMA, and two variants of a combined CUSUM-EWMA algorithm. These algorithms have been evaluated on ten benchmark datasets. The F-Score for each of the algorithms has been used to measure their performance appropriately. The preliminary experimental results prove to be promising for the proposed method in detecting anomalies from time series data.