A Comparative Study on Parameter Selection and Outlier Removal for Change Point Detection in Time Series

Abstract

Change point analysis is an efficient method for understanding the unexpected behaviour of the data used in many different disciplines. Although the literature contains a variety of change point analysis methods, there are relatively fewer studies that focus on the performance of parameter selection and outlier removal that are applied on real data sets. In this study two methods based on regression and statistical properties are proposed and compared with a method using Bayesian approach to evaluate their performance on the selection of parameters and removal of outliers. The methods are executed using different parameters on the well-log data set with and without outliers that are removed either manually or automatically. The results show that different data sets require different parameters to locate their change points. The proposed methods have intuitive parameters to control the algorithm, run faster, and do not require any assumptions to be made such as maximum number of change points. These properties also make them good candidates for online change point analysis.