Open AccessAdaptive Filtering Method using of Non- Gaussian Moving Average model from First order (simulation study)
Author(s) -
Wadhah S. Ibraheem
Publication year - 2021
Publication title -
magallaẗ kulliyyaẗ al-rāfidayn al-ǧāmi'aẗ al-'ulūm/maǧallaẗ kulliyyaẗ al-rāfidayn al-ǧāmiʻaẗ li-l-ʻulūm
Language(s) - English
Resource type - Journals
eISSN - 2790-2293
pISSN - 1681-6870
DOI - 10.55562/jrucs.v28i2.393
Subject(s) - series (stratigraphy) , estimator , moving average model , moving average , gaussian , parametric statistics , algorithm , computer science , mathematics , parametric model , time series , statistics , autoregressive integrated moving average , paleontology , physics , quantum mechanics , biology
The interest of the time series focuses on the relations that linking the variables phenomenon and time, and to build a model suitable for time series requires the attention of parametric phenomenon, and Adaptive filtering method is one of the most important ways to build time series, In this paper, we used the Adaptive filtering method of non-Gaussian moving average model from first order MA (1 ) for several distributions of intermittent and continuous for a number of different volumes of samples and using simulation techniques.The values of estimators by Iterative process and by Adaptive filtering method increases as the size of the sample increased, The values MSE and MAPE to MA(1) model decrease as the sample size is increased, and for all kinds of discrete and continuous distributions.Keyword: Time series, Moving Average model, Iterative process, Adaptive filtering