Trend analysis and change point techniques: a survey

Abstract

Trend analysis and change point detection in a time series are frequent analysis tools. Change point detection is the identification of abrupt variation in the process behavior due to distributional or structural changes, whereas trend can be defined as estimation of gradual departure from past norms. We examine four different change point detection methods which, by virtue of current literature, appear to be the most widely used and the newest algorithms. They are Wild Binary Segmentation, E-Agglomerative algorithm for change point, Iterative Robust Detection method and Bayesian Analysis of Change Points. We measure the power and accuracy of these current methods using simulated data. We draw comparisons on the functionality and usefulness of each method. We also analyze the data in the presence of trend, using Mann–Kendall and Cox–Stuart methods together with the change point algorithms, in order to evaluate whether presence of trend affects change point or vice versa.