Recurrent error-based ridge polynomial neural networks for time series forecasting

Time series forecasting has attracted much attention due to its impact on many practical applications. Neural networks (NNs) have been attracting widespread interest as a promising tool for time series forecasting. The majority of NNs employ only autoregressive (AR) inputs (i.e., lagged time seri...

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Main Author:	Hassan Saeed, Waddah Waheeb
Format:	Thesis
Language:	English English English
Published:	2019
Subjects:	QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/133/1/24p%20WADDAH%20WAHEEB%20HASSAN%20SAEED.pdf http://eprints.uthm.edu.my/133/2/WADDAH%20WAHEEB%20HASSAN%20SAEED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/133/3/WADDAH%20WAHEEB%20HASSAN%20SAEED%20WATERMARK.pdf
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id	my-uthm-ep.133
record_format	uketd_dc
spelling	my-uthm-ep.1332021-07-05T02:24:24Z Recurrent error-based ridge polynomial neural networks for time series forecasting 2019-04 Hassan Saeed, Waddah Waheeb QA76 Computer software Time series forecasting has attracted much attention due to its impact on many practical applications. Neural networks (NNs) have been attracting widespread interest as a promising tool for time series forecasting. The majority of NNs employ only autoregressive (AR) inputs (i.e., lagged time series values) when forecasting time series. Moving-average (MA) inputs (i.e., errors) however have not adequately considered. The use of MA inputs, which can be done by feeding back forecasting errors as extra network inputs, alongside AR inputs help to produce more accurate forecasts. Among numerous existing NNs architectures, higher order neural networks (HONNs), which have a single layer of learnable weights, were considered in this research work as they have demonstrated an ability to deal with time series forecasting and have an simple architecture. Based on two HONNs models, namely the feedforward ridge polynomial neural network (RPNN) and the recurrent dynamic ridge polynomial neural network (DRPNN), two recurrent error-based models were proposed. These models were called the ridge polynomial neural network with error feedback (RPNN-EF) and the ridge polynomial neural network with error-output feedbacks (RPNN-EOF). Extensive simulations covering ten time series were performed. Besides RPNN and DRPNN, a pi-sigma neural network and a Jordan pi-sigma neural network were used for comparison. Simulation results showed that introducing error feedback to the models lead to significant forecasting performance improvements. Furthermore, it was found that the proposed models outperformed many state-of-the-art models. It was concluded that the proposed models have the capability to efficiently forecast time series and that practitioners could benefit from using these forecasting models. 2019-04 Thesis http://eprints.uthm.edu.my/133/ http://eprints.uthm.edu.my/133/1/24p%20WADDAH%20WAHEEB%20HASSAN%20SAEED.pdf text en public http://eprints.uthm.edu.my/133/2/WADDAH%20WAHEEB%20HASSAN%20SAEED%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/133/3/WADDAH%20WAHEEB%20HASSAN%20SAEED%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Hassan Saeed, Waddah Waheeb Recurrent error-based ridge polynomial neural networks for time series forecasting
description	Time series forecasting has attracted much attention due to its impact on many practical applications. Neural networks (NNs) have been attracting widespread interest as a promising tool for time series forecasting. The majority of NNs employ only autoregressive (AR) inputs (i.e., lagged time series values) when forecasting time series. Moving-average (MA) inputs (i.e., errors) however have not adequately considered. The use of MA inputs, which can be done by feeding back forecasting errors as extra network inputs, alongside AR inputs help to produce more accurate forecasts. Among numerous existing NNs architectures, higher order neural networks (HONNs), which have a single layer of learnable weights, were considered in this research work as they have demonstrated an ability to deal with time series forecasting and have an simple architecture. Based on two HONNs models, namely the feedforward ridge polynomial neural network (RPNN) and the recurrent dynamic ridge polynomial neural network (DRPNN), two recurrent error-based models were proposed. These models were called the ridge polynomial neural network with error feedback (RPNN-EF) and the ridge polynomial neural network with error-output feedbacks (RPNN-EOF). Extensive simulations covering ten time series were performed. Besides RPNN and DRPNN, a pi-sigma neural network and a Jordan pi-sigma neural network were used for comparison. Simulation results showed that introducing error feedback to the models lead to significant forecasting performance improvements. Furthermore, it was found that the proposed models outperformed many state-of-the-art models. It was concluded that the proposed models have the capability to efficiently forecast time series and that practitioners could benefit from using these forecasting models.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hassan Saeed, Waddah Waheeb
author_facet	Hassan Saeed, Waddah Waheeb
author_sort	Hassan Saeed, Waddah Waheeb
title	Recurrent error-based ridge polynomial neural networks for time series forecasting
title_short	Recurrent error-based ridge polynomial neural networks for time series forecasting
title_full	Recurrent error-based ridge polynomial neural networks for time series forecasting
title_fullStr	Recurrent error-based ridge polynomial neural networks for time series forecasting
title_full_unstemmed	Recurrent error-based ridge polynomial neural networks for time series forecasting
title_sort	recurrent error-based ridge polynomial neural networks for time series forecasting
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2019
url	http://eprints.uthm.edu.my/133/1/24p%20WADDAH%20WAHEEB%20HASSAN%20SAEED.pdf http://eprints.uthm.edu.my/133/2/WADDAH%20WAHEEB%20HASSAN%20SAEED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/133/3/WADDAH%20WAHEEB%20HASSAN%20SAEED%20WATERMARK.pdf
_version_	1747830537134800896

Recurrent error-based ridge polynomial neural networks for time series forecasting

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