Outliers in bilinear time series mode / Ibrahim Mohamed

This study has two main objectives: Model building and detection of outliers in BL(1,1,1,1) models in time-domain frame work. In model building, the Box-Jenkins approach was closely followed. In the identification stage, time-domain based nonlinearity tests were considered to distinguish nonlinearit...

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Main Author:	Mohamed, Ibrahim
Format:	Thesis
Language:	English
Published:	2005
Subjects:	Technological change
Online Access:	https://ir.uitm.edu.my/id/eprint/54400/1/54400.PDF
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spelling	my-uitm-ir.544002022-05-04T14:33:19Z Outliers in bilinear time series mode / Ibrahim Mohamed 2005-03 Mohamed, Ibrahim Technological change This study has two main objectives: Model building and detection of outliers in BL(1,1,1,1) models in time-domain frame work. In model building, the Box-Jenkins approach was closely followed. In the identification stage, time-domain based nonlinearity tests were considered to distinguish nonlinearity data set from linear time series data. In general, identifying the order of bilinear model including BL(1,1,1,1) model is not possible yet due to the complexity of form taken by the moments of bilinear model. In the estimation stage ,the nonlinear least squares method were used to estimate the parameters of BL(1,1,1,1) models. In the diagnostic stage, the residuals were examined to check the adequacy of model. In addition, the Akaike's information criteria, the Akaike's Bayesian information criteria and Schwarz's criterion were used for model comparison purposes. Outliers exist due to many possibilities such as mis recording, disaster or changes of nature. The occurrence of four types of outliers; the additive outlier(AO), innovational outlier (10), level change (LC) and temporary change (TC), in data from BL(1,1,1,1) models were considered in the study. Their features were studied so that different patterns caused by each type of outliers were distinguishable. Further, the measures of outlier effect for AO, 10, LC and TC were derived using the least squares method. Their variances were obtained using bootstrap method. The detection of outliers was carried out by examining the maximum value of the standardized statistics of the outlier effects. The outlier detection procedure for identifying the type of outlier at time point t was proposed. Simulation studies were carried out to study the performance of the procedure in BL(1,1,1,1) models. It was found out that, in general, the proposed procedure performed well in detecting outliers. As for illustration, the proposed procedure was applied on three hydrological data. 2005-03 Thesis https://ir.uitm.edu.my/id/eprint/54400/ https://ir.uitm.edu.my/id/eprint/54400/1/54400.PDF text en public phd doctoral Universiti Teknologi MARA Faculty of Information Technology and Quantitative Sciences Zaharim, Azami (Dr. )
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Zaharim, Azami (Dr. )
topic	Technological change
spellingShingle	Technological change Mohamed, Ibrahim Outliers in bilinear time series mode / Ibrahim Mohamed
description	This study has two main objectives: Model building and detection of outliers in BL(1,1,1,1) models in time-domain frame work. In model building, the Box-Jenkins approach was closely followed. In the identification stage, time-domain based nonlinearity tests were considered to distinguish nonlinearity data set from linear time series data. In general, identifying the order of bilinear model including BL(1,1,1,1) model is not possible yet due to the complexity of form taken by the moments of bilinear model. In the estimation stage ,the nonlinear least squares method were used to estimate the parameters of BL(1,1,1,1) models. In the diagnostic stage, the residuals were examined to check the adequacy of model. In addition, the Akaike's information criteria, the Akaike's Bayesian information criteria and Schwarz's criterion were used for model comparison purposes. Outliers exist due to many possibilities such as mis recording, disaster or changes of nature. The occurrence of four types of outliers; the additive outlier(AO), innovational outlier (10), level change (LC) and temporary change (TC), in data from BL(1,1,1,1) models were considered in the study. Their features were studied so that different patterns caused by each type of outliers were distinguishable. Further, the measures of outlier effect for AO, 10, LC and TC were derived using the least squares method. Their variances were obtained using bootstrap method. The detection of outliers was carried out by examining the maximum value of the standardized statistics of the outlier effects. The outlier detection procedure for identifying the type of outlier at time point t was proposed. Simulation studies were carried out to study the performance of the procedure in BL(1,1,1,1) models. It was found out that, in general, the proposed procedure performed well in detecting outliers. As for illustration, the proposed procedure was applied on three hydrological data.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohamed, Ibrahim
author_facet	Mohamed, Ibrahim
author_sort	Mohamed, Ibrahim
title	Outliers in bilinear time series mode / Ibrahim Mohamed
title_short	Outliers in bilinear time series mode / Ibrahim Mohamed
title_full	Outliers in bilinear time series mode / Ibrahim Mohamed
title_fullStr	Outliers in bilinear time series mode / Ibrahim Mohamed
title_full_unstemmed	Outliers in bilinear time series mode / Ibrahim Mohamed
title_sort	outliers in bilinear time series mode / ibrahim mohamed
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Information Technology and Quantitative Sciences
publishDate	2005
url	https://ir.uitm.edu.my/id/eprint/54400/1/54400.PDF
_version_	1783734892846120960

Outliers in bilinear time series mode / Ibrahim Mohamed

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