Next Article in Journal
The Impact of the Covariance Matrix Sampling on the Angle of Arrival Estimation Accuracy
Previous Article in Journal
Comparison of Lithium-Ion Battery Models for Simulating Storage Systems in Distributed Power Generation
Open AccessArticle

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

1
Department of Industrial Engineering, Hanyang University, 222 Wangsimni-Ro, Seoul 133-791, Korea
2
Department of Industrial & Management Engineering, Hanyang University, Ansan, Gyeonggi-do 15588, Korea
*
Author to whom correspondence should be addressed.
Inventions 2019, 4(3), 42; https://doi.org/10.3390/inventions4030042
Received: 11 June 2019 / Revised: 1 August 2019 / Accepted: 2 August 2019 / Published: 8 August 2019
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. View Full-Text
Keywords: probabilistic method; bayesian statistical modeling; change point detection; likelihood ratio test; time series analysis probabilistic method; bayesian statistical modeling; change point detection; likelihood ratio test; time series analysis
Show Figures

Figure 1

MDPI and ACS Style

Khan, M.R.; Sarkar, B. Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method. Inventions 2019, 4, 42.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop