Developing defuzzifying method of fuzzy time-variant series for forecasting product demand

This thesis is about proposing a method of defuzzifying fuzzy time-variant series on forecasting demand by developing time-variant model considering Mean Absolute Error (MAE) and trend of primary forecasting (MAE&Trend). The previous method of defuzzifying was based on Artificial Neural Network...

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Main Author:	Saeed, Akbarnatajbisheh
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/41814/5/SaeedAkbarnatajbishehMFKM2013.pdf
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id	my-utm-ep.41814
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spelling	my-utm-ep.418142017-07-17T08:05:46Z Developing defuzzifying method of fuzzy time-variant series for forecasting product demand 2013-06 Saeed, Akbarnatajbisheh QA Mathematics This thesis is about proposing a method of defuzzifying fuzzy time-variant series on forecasting demand by developing time-variant model considering Mean Absolute Error (MAE) and trend of primary forecasting (MAE&Trend). The previous method of defuzzifying was based on Artificial Neural Network (ANN). Defuzzifying by ANN needs model identification which is time consuming. To justify the robustness of the proposed method, it is compared with Song‘s and Chen‘s fuzzy time-invariant methods as well as Autoregressive Integrated Moving Average (ARIMA) time series method. Advantage of fuzzy method is its robustness in covering large range of data including linguistic data as well as numerical and its ability to forecast by small amount of data. Accuracy of the methods is achieved by comparing Mean Absolute Percentage Error (MAPE). The MAPE results are modified by omitting outranged data to have more logical evaluations. Based on the evaluations, the proposed model of MAE&Trend shows better accuracy in forecasting demand. 2013-06 Thesis http://eprints.utm.my/id/eprint/41814/ http://eprints.utm.my/id/eprint/41814/5/SaeedAkbarnatajbishehMFKM2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Mechanical Engineering Faculty of Mechanical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Saeed, Akbarnatajbisheh Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
description	This thesis is about proposing a method of defuzzifying fuzzy time-variant series on forecasting demand by developing time-variant model considering Mean Absolute Error (MAE) and trend of primary forecasting (MAE&Trend). The previous method of defuzzifying was based on Artificial Neural Network (ANN). Defuzzifying by ANN needs model identification which is time consuming. To justify the robustness of the proposed method, it is compared with Song‘s and Chen‘s fuzzy time-invariant methods as well as Autoregressive Integrated Moving Average (ARIMA) time series method. Advantage of fuzzy method is its robustness in covering large range of data including linguistic data as well as numerical and its ability to forecast by small amount of data. Accuracy of the methods is achieved by comparing Mean Absolute Percentage Error (MAPE). The MAPE results are modified by omitting outranged data to have more logical evaluations. Based on the evaluations, the proposed model of MAE&Trend shows better accuracy in forecasting demand.
format	Thesis
qualification_level	Master's degree
author	Saeed, Akbarnatajbisheh
author_facet	Saeed, Akbarnatajbisheh
author_sort	Saeed, Akbarnatajbisheh
title	Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
title_short	Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
title_full	Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
title_fullStr	Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
title_full_unstemmed	Developing defuzzifying method of fuzzy time-variant series for forecasting product demand
title_sort	developing defuzzifying method of fuzzy time-variant series for forecasting product demand
granting_institution	Universiti Teknologi Malaysia, Faculty of Mechanical Engineering
granting_department	Faculty of Mechanical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/41814/5/SaeedAkbarnatajbishehMFKM2013.pdf
_version_	1747816623895478272

Developing defuzzifying method of fuzzy time-variant series for forecasting product demand

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