An improved Pi-Sigma neural network using error feedback for time series prediction

Time series prediction grabs much attention because of its effect on the vast range of real-life applications. Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that decrease the performance of the model. Higher Order Neural N...

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Main Author:	Akram, Urooj
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA75-76.95 Calculating machines
Online Access:	http://eprints.uthm.edu.my/523/1/24p%20UROOJ%20AKRAM.pdf http://eprints.uthm.edu.my/523/2/UROOJ%20AKRAM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/523/3/UROOJ%20AKRAM%20WATERMARK.pdf
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spelling	my-uthm-ep.5232021-07-25T08:41:54Z An improved Pi-Sigma neural network using error feedback for time series prediction 2018-04 Akram, Urooj QA75-76.95 Calculating machines Time series prediction grabs much attention because of its effect on the vast range of real-life applications. Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that decrease the performance of the model. Higher Order Neural Network (HONN) using recurrent feedback appeared as a powerful technique in the domain of time series prediction and it has the ability to expand the input space, making them more efficient for solving complex problems and perform high learning abilities in time series prediction. This study proposed a model called improved Pi-Sigma Neural Network using Error Feedback (PSNN-EF) which combines the properties of Pi-Sigma Neural Network (PSNN), recurrence and error feedback. PSNN-EF uses backpropagation gradient descent algorithm for training purpose and is tested with physical time series signals of humidity, evaporation and wind direction datasets that are collected from Malaysian Meteorological Department (MMD). The prediction result is compared with Jordan Pi-Sigma Neural Network (JPSN) and the ordinary PSNN. The results clearly showed that the PSNN-EF significantly improved the computational efficiency of the training process and has been developed to produce more realistic and acceptable results. The average improvement of the proposed model on evaporation dataset is 2.06%, humidity is 7.45% and wind is 3.51% as compared to other models. The benefit of using error feedback is that it generates more accurate and promising results of prediction. Therefore, from the performance of the proposed method, it is noticed that PSNN-EF can provide better solution to JPSN for one-step-ahead prediction of those three datasets. 2018-04 Thesis http://eprints.uthm.edu.my/523/ http://eprints.uthm.edu.my/523/1/24p%20UROOJ%20AKRAM.pdf text en public http://eprints.uthm.edu.my/523/2/UROOJ%20AKRAM%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/523/3/UROOJ%20AKRAM%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA75-76.95 Calculating machines
spellingShingle	QA75-76.95 Calculating machines Akram, Urooj An improved Pi-Sigma neural network using error feedback for time series prediction
description	Time series prediction grabs much attention because of its effect on the vast range of real-life applications. Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that decrease the performance of the model. Higher Order Neural Network (HONN) using recurrent feedback appeared as a powerful technique in the domain of time series prediction and it has the ability to expand the input space, making them more efficient for solving complex problems and perform high learning abilities in time series prediction. This study proposed a model called improved Pi-Sigma Neural Network using Error Feedback (PSNN-EF) which combines the properties of Pi-Sigma Neural Network (PSNN), recurrence and error feedback. PSNN-EF uses backpropagation gradient descent algorithm for training purpose and is tested with physical time series signals of humidity, evaporation and wind direction datasets that are collected from Malaysian Meteorological Department (MMD). The prediction result is compared with Jordan Pi-Sigma Neural Network (JPSN) and the ordinary PSNN. The results clearly showed that the PSNN-EF significantly improved the computational efficiency of the training process and has been developed to produce more realistic and acceptable results. The average improvement of the proposed model on evaporation dataset is 2.06%, humidity is 7.45% and wind is 3.51% as compared to other models. The benefit of using error feedback is that it generates more accurate and promising results of prediction. Therefore, from the performance of the proposed method, it is noticed that PSNN-EF can provide better solution to JPSN for one-step-ahead prediction of those three datasets.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Akram, Urooj
author_facet	Akram, Urooj
author_sort	Akram, Urooj
title	An improved Pi-Sigma neural network using error feedback for time series prediction
title_short	An improved Pi-Sigma neural network using error feedback for time series prediction
title_full	An improved Pi-Sigma neural network using error feedback for time series prediction
title_fullStr	An improved Pi-Sigma neural network using error feedback for time series prediction
title_full_unstemmed	An improved Pi-Sigma neural network using error feedback for time series prediction
title_sort	improved pi-sigma neural network using error feedback for time series prediction
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Computer Science and Information Technology
publishDate	2018
url	http://eprints.uthm.edu.my/523/1/24p%20UROOJ%20AKRAM.pdf http://eprints.uthm.edu.my/523/2/UROOJ%20AKRAM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/523/3/UROOJ%20AKRAM%20WATERMARK.pdf
_version_	1747830630962429952

An improved Pi-Sigma neural network using error feedback for time series prediction

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