Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method

Abstract

Unpredicted deviations in time series data are called change points. These unexpected changes indicate transitions between states. Change point detection is a valuable technique in modeling to estimate unanticipated property changes underlying time series data. It can be applied in different areas like climate change detection, human activity analysis, medical condition monitoring and speech and image analyses. Supervised and unsupervised techniques are equally used to identify changes in time series. Even though change point detection algorithms have improved considerably in recent years, several undefended challenges exist. Previous work on change point detection was limited to specific areas; therefore, more studies are required to investigate appropriate change point detection techniques applicable to any data distribution to assess the numerical productivity of any stochastic process. This research is primarily focused on the formulation of an innovative methodology for change point detection of diversely distributed stochastic processes using a probabilistic method with variable data structures. Bayesian inference and a likelihood ratio test are used to detect a change point at an unknown time (k). The likelihood of k is determined and used in the likelihood ratio test. Parameter change must be evaluated by critically analyzing the parameters expectations before and after a change point. Real-time data of particulate matter concentrations at different locations were used for numerical verification, due to diverse features, that is, environment, population densities and transportation vehicle densities. Therefore, this study provides an understanding of how well this recommended model could perform for different data structures.

Analysis of the Environmental Effects of the Clean Heating Policy in Northern China

“…The policies they researched were early ones, such as in 2010, or in a specific city such as Beijing [14,15]. There are some methods for analyzing clean air policies, such as the Bayesian approach, life cycle assessment, the pollutant emission intensity generated by heating, the slack-based measure-directional distance function, the computable general equilibrium model, and the quasi-natural experiment method [14][15][16][17][18][19][20][21]. The above methods need to carry out a sophisticated social and economic system modeling for the research object.…”