مونا ایوبی
مونا ایوبی
استادیار صنایع

Change Point Estimation of the Stationary State in Auto Regressive Moving Average (ARMA) Models, using Maximum Likelihood Estimation and SVD-based Filtering

نویسندگانReza Sheikhrabori, Majid Aminnayeri, Mona Ayoubi
نشریهInternational Journal of Engineering
نوع مقالهFull Paper
تاریخ انتشار2018
رتبه نشریهISI
نوع نشریهالکترونیکی
کشور محل چاپایران
چکیده مقاله

In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of ARMA (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state’s change point. To estimate unidentified parameters following the change point, the Dynamic Linear Model’s Filtering was utilized on the basis of the singular decomposition of values. The proposed model has wide applications in several fields such as finance, stock exchange marks and rapid production. The results of simulation showed the suggested estimator’s effectiveness. In addition, a real example on stock exchange market is offered to delineate the application.