Bayesian and Expectation Maximization methods for multivariate change point detection

Keshavarz, Marziyeh; Huang, Biao

doi:10.1016/j.compchemeng.2013.09.012

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…In this section, the main idea of the EM algorithm was introduced in [22][23][24][25][26][27][28]. Then we make use of the Kalman filtering and Kalman smoothing approaches to derive the iterative computation procedure for the proposed model (1) and (2).…”

Section: Em Algorithm For Parameter Identificationmentioning

confidence: 99%

Stochastic dynamic modeling of lithium battery via expectation maximization algorithm

Liu

Cai

et al. 2016

Neurocomputing

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Section: Em Algorithm For Parameter Identificationmentioning

confidence: 99%

Stochastic dynamic modeling of lithium battery via expectation maximization algorithm

Liu

Cai

et al. 2016

Neurocomputing

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“…A comparison of Expectation Maximization (EM) method and Bayesian method for change point detection of multivariate data was done. The Bayesian technique involves fewer computational work, while EM reveals better performance for unsuitable priors and minor changes [25]. Min-max distribution free continuous-review model was presented with a service level constraint and variable lead time [26].…”

Section: Related Literaturementioning

confidence: 99%

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

Khan

Sarkar

2019

Inventions

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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.

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“…Regime switching, mixtures, or applications of change point models in other fields are vastly common especially in finance, economics, engineering, and sciences (Clements and Hendry (1996); Hamilton (2008, Keshavarz and Huang (2014)). In transportation, two state mean adapting model with EM is developed in (Cetin and Comert (2007) and Comert and Bezuglov (2013)) combined a four state HMM, EM, and ARIMA (i.e., Online Change Point Based (OCPB) model) and compared against mean adapting and simple time series models.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive traffic parameter prediction: Effect of number of states and transferability of models

Comert

Bezuglov

Cetin

2016

Transportation Research Part C: Emerging Technologies

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Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset.

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Bayesian and Expectation Maximization methods for multivariate change point detection

Cited by 18 publications

References 20 publications

Stochastic dynamic modeling of lithium battery via expectation maximization algorithm

Stochastic dynamic modeling of lithium battery via expectation maximization algorithm

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

Adaptive traffic parameter prediction: Effect of number of states and transferability of models

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