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<p dir="ltr" style="text-align: justify; margin-right: -19px; margin-left: -20px;"><span style="color:#4e5f70;"><span style="font-size:11pt"><span style="text-justify:inter-ideograph"><span style="line-height:normal"><span style="font-family:&quot;Calibri&quot;,sans-serif"><span style="font-size:8.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="background-color:#ffffff;">In this paper, for the first time, the subject of change point estimation has been utilized in</span></span></span><span style="font-size:8.0pt"><span style="background-color:#ffffff;"> the </span></span><span style="font-size:8.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="background-color:#ffffff;">stationary state of ARMA (1, 1). In the monitoring phase, in case the </span></span></span><span style="font-size:8.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="background-color:#ffffff;">features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the </span></span></span><span style="font-size:8.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="background-color:#ffffff;">maximum likelihood technique,</span></span></span><span style="font-size:8.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="background-color:#ffffff;"> an approach will be developed for the </span></span></span><span style="font-size:8.0pt"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="background-color:#ffffff;">estimation of the stationary state&rsquo;s change point. To estimate unidentified parameters following the change point, the Dynamic Linear Model&rsquo;s Filtering was utilized on the basis of the singular decomposition of values. </span><span style="background:yellow"><span style="background-color:#ffffff;">The proposed model has wide applications in several fields such as finance, stock exchange marks and rapid production.</span></span><span style="background-color:#ffffff;"> The results of simulation showed the suggested estimator&rsquo;s effectiveness. In addition, a real example on stock exchange market is offered to delineate the application.</span></span></span></span></span></span></span></span></p>