New optimized model identification in time series model and its difficulties
محورهای موضوعی : Stochastics Problems
احمدرضا زنبوری
1
(
گروه آمار، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران
)
کریم زارع
2
(
گروه آمار، واحد مرودشت، دانشگاه آزاد اسلامی، مرودشت، ایران
)
کلید واژه: Time series, Outliers, Box-Jenkins, Extended sample autocorrelation function,
چکیده مقاله :
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.
در مدل خودبازگشتی-میانگین متحرک (arima) شناسایی مدل یکی از مراحل پیچیده می باشد . این مرحله بخصوص برای مدل های امیخته پیچیده تر می باشد . در این مقاله ابتدا روش باکس جنکینز را شرح داده و سپس به مشکل شناسایی مدل در این روش می پردازیم و در ادامه به بررسی مشکل باقیمانده ها در مدل های سری زمانی می پردازیم . با استفاده از این روش بهینه این مشکلات را حل می کنیم . روش استفاده شده در این مقاله در زمینه زمان و سرعت ، برتری مطلوبی نسبت به روش باکس جنکینز را دارا می باشد .
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