Median-difference window subseries score for contextual anomaly on time series

Abstract

Anomaly detection on time series is one of the exciting topics in data mining. The aim is to find a data point which is different from the majority, called an anomaly. In this thesis, a novel anomaly score called Median-Difference Window subseries Score (MDWS) is proposed with its algorithm together with the parameter of the recommended window length for detecting the contextual anomalies on time series data. It is computed as the subtraction of the middle-window point with the median of all data points within the current window. The proposed MDWS algorithm is implemented as the median-update of the current window subseries to maintain the linear time complexity. Two anomaly thresholds are applied from interquartile range rule. The experimental results show that the MDWS has the highest performance on both synthetic and real world benchmark datasets from Yahoo! and Numenta comparing with others existing anomaly detection methods. Moreover, MDWS algorithm is also faster than other algorithm on the large dataset.