New optimized model identification in time series model and its difficulties

Iranian Journal of Optimization

مقاله 4، دوره 9، شماره 1، شهریور 2017، صفحه 39-47   

نوع مقاله: Research Paper

نویسندگان

Ahmadreza Zanboori¹؛ Karim Zare^{* 2}

¹Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran.

²Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran

چکیده

Model identification is an important and complicated step within the autoregressive integrated moving average (ARIMA) methodology framework. This step is especially difficult for integrated series. In this article first investigate Box-Jenkins methodology and its faults in detecting model, and hence have discussed the problem of outliers in time series. By using this optimization method, we will overcome this problem. The method that used in this paper is better than the Box-Jenkins in term of optimality time.

کلیدواژه‌ها

Time series؛ Outliers؛ Box-Jenkins؛ Extended sample autocorrelation function

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