Hybridization of nonlinear and linear models for time series forecasting

Nowadays, time series forecasting is very challenging due to the uncertainties of real world events that are influenced by many indefinite factors and rapid changes. This scenario requires forecasting methods that work efficiently with incomplete and multivariate data. Otherwise, the solutions will...

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Main Author:	Salleh@Sallehuddin, Roselina
Format:	Thesis
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/18768/1/HamisanSalimMFP2010.pdf
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id	my-utm-ep.18768
record_format	uketd_dc
spelling	my-utm-ep.187682017-09-13T06:31:26Z Hybridization of nonlinear and linear models for time series forecasting 2010 Salleh@Sallehuddin, Roselina QA75 Electronic computers. Computer science Nowadays, time series forecasting is very challenging due to the uncertainties of real world events that are influenced by many indefinite factors and rapid changes. This scenario requires forecasting methods that work efficiently with incomplete and multivariate data. Otherwise, the solutions will tend to trap into a local minimum or will encounter over fitting problems that would lead to inaccurate predictions. Due to the complexity of time series data, there is no single method that best fit all situations. Therefore, a hybrid model is proposed to improve individual model performance. However, most of the existing hybrid models utilize univariate data which requires huge historical data to obtain accurate forecasting result. Thus, in this research, a new hybrid model is developed by combining nonlinear and linear models to overcome the current discrepancies of the existing hybrid models. The proposed hybrid model integrates the nonlinear Grey Backpropagation Particle Swarm Optimization and linear Autoregressive Integrated Moving Average model (GBPSO_ARIMA) by combining new features. These features include multivariate time series data, cooperative feature selection (CFS), optimization algorithm (PSO) and hybrid sequence process. The proposed model is tested on four different sets of time series data that exhibit different time series behaviours and data scales. The performance of the proposed model is analysed and compared with the individual and hybrid models. The experiments show that the proposed hybrid model is more robust for all datasets regardless of the sample sizes and data behaviours. The validation of these results is further tested using hypothesis test and analysed in terms of stability and adaptability. The results have proven that the proposed hybrid model provides a better alternative tool for time series forecasting. This is due to its promising performance and robust capability in handling multivariate small and large scale time series data in incomplete data situations, which is also capable in reducing over fitting issues and local minimum problems. 2010 Thesis http://eprints.utm.my/id/eprint/18768/ http://eprints.utm.my/id/eprint/18768/1/HamisanSalimMFP2010.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Salleh@Sallehuddin, Roselina Hybridization of nonlinear and linear models for time series forecasting
description	Nowadays, time series forecasting is very challenging due to the uncertainties of real world events that are influenced by many indefinite factors and rapid changes. This scenario requires forecasting methods that work efficiently with incomplete and multivariate data. Otherwise, the solutions will tend to trap into a local minimum or will encounter over fitting problems that would lead to inaccurate predictions. Due to the complexity of time series data, there is no single method that best fit all situations. Therefore, a hybrid model is proposed to improve individual model performance. However, most of the existing hybrid models utilize univariate data which requires huge historical data to obtain accurate forecasting result. Thus, in this research, a new hybrid model is developed by combining nonlinear and linear models to overcome the current discrepancies of the existing hybrid models. The proposed hybrid model integrates the nonlinear Grey Backpropagation Particle Swarm Optimization and linear Autoregressive Integrated Moving Average model (GBPSO_ARIMA) by combining new features. These features include multivariate time series data, cooperative feature selection (CFS), optimization algorithm (PSO) and hybrid sequence process. The proposed model is tested on four different sets of time series data that exhibit different time series behaviours and data scales. The performance of the proposed model is analysed and compared with the individual and hybrid models. The experiments show that the proposed hybrid model is more robust for all datasets regardless of the sample sizes and data behaviours. The validation of these results is further tested using hypothesis test and analysed in terms of stability and adaptability. The results have proven that the proposed hybrid model provides a better alternative tool for time series forecasting. This is due to its promising performance and robust capability in handling multivariate small and large scale time series data in incomplete data situations, which is also capable in reducing over fitting issues and local minimum problems.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Salleh@Sallehuddin, Roselina
author_facet	Salleh@Sallehuddin, Roselina
author_sort	Salleh@Sallehuddin, Roselina
title	Hybridization of nonlinear and linear models for time series forecasting
title_short	Hybridization of nonlinear and linear models for time series forecasting
title_full	Hybridization of nonlinear and linear models for time series forecasting
title_fullStr	Hybridization of nonlinear and linear models for time series forecasting
title_full_unstemmed	Hybridization of nonlinear and linear models for time series forecasting
title_sort	hybridization of nonlinear and linear models for time series forecasting
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2010
url	http://eprints.utm.my/id/eprint/18768/1/HamisanSalimMFP2010.pdf
_version_	1747815354088816640

Hybridization of nonlinear and linear models for time series forecasting

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