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:
Online Access:http://eprints.utm.my/id/eprint/41814/5/SaeedAkbarnatajbishehMFKM2013.pdf
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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
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