Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib

Forecasting is important to minimize the uncertainty in decision making. Various forecasting methods have been proposed and one of them is the forecasting model using fuzzy time series (FTS). FTS forecasting model can cater for data in linguistic values. However, most of the FTS forecasting models u...

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Main Author:	Ab Mutalib, Siti Musleha
Format:	Thesis
Language:	English
Published:	2017
Online Access:	https://ir.uitm.edu.my/id/eprint/89135/1/89135.pdf
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spelling	my-uitm-ir.891352024-04-19T09:24:28Z Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib 2017 Ab Mutalib, Siti Musleha Forecasting is important to minimize the uncertainty in decision making. Various forecasting methods have been proposed and one of them is the forecasting model using fuzzy time series (FTS). FTS forecasting model can cater for data in linguistic values. However, most of the FTS forecasting models use discrete fuzzy sets as a basis for calculating the forecasted values and, thus, cannot provide the forecasted range under different degrees of confidence. Besides, in order to calculate the forecasting accuracy such as mean square error (MSE) and mean absolute percentage error (MAPE), the forecasted values need to be defuzzified. During the defuzzification process, some information could be lost from the data due to the simplification process. Hence, this contributes to its inability to grasp the sense of uncertainty that has been kept throughout the forecasting procedure. In this study, we propose an FTS forecasting model based on trapezoidal fuzzy numbers with a similarity measure approach. Three methods of partitioning the intervals are used which are average based length, frequency density based and re-divide randomly chosen length. Furthermore, the order of relation of FTS is also considered which are the first order and the second order relation. The forecasting accuracy is evaluated using degree of similarity between fuzzy actual value and fuzzy forecasted value. A combination of three fuzzy similarity measures namely the centre of gravity, area and height is used to evaluate the forecasting accuracy. For implementation purposes, this proposed model is applied to forecast the unemployment rate in Malaysia from the year 1982 to 2013. The result shows that the interval length and the order of FTS will affect the forecasting accuracy. Moreover, the similarity measure concept can be used as an alternative approach to measure the accuracy without going through the defuzzification process. 2017 Thesis https://ir.uitm.edu.my/id/eprint/89135/ https://ir.uitm.edu.my/id/eprint/89135/1/89135.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Ramli, Nazirah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ramli, Nazirah
description	Forecasting is important to minimize the uncertainty in decision making. Various forecasting methods have been proposed and one of them is the forecasting model using fuzzy time series (FTS). FTS forecasting model can cater for data in linguistic values. However, most of the FTS forecasting models use discrete fuzzy sets as a basis for calculating the forecasted values and, thus, cannot provide the forecasted range under different degrees of confidence. Besides, in order to calculate the forecasting accuracy such as mean square error (MSE) and mean absolute percentage error (MAPE), the forecasted values need to be defuzzified. During the defuzzification process, some information could be lost from the data due to the simplification process. Hence, this contributes to its inability to grasp the sense of uncertainty that has been kept throughout the forecasting procedure. In this study, we propose an FTS forecasting model based on trapezoidal fuzzy numbers with a similarity measure approach. Three methods of partitioning the intervals are used which are average based length, frequency density based and re-divide randomly chosen length. Furthermore, the order of relation of FTS is also considered which are the first order and the second order relation. The forecasting accuracy is evaluated using degree of similarity between fuzzy actual value and fuzzy forecasted value. A combination of three fuzzy similarity measures namely the centre of gravity, area and height is used to evaluate the forecasting accuracy. For implementation purposes, this proposed model is applied to forecast the unemployment rate in Malaysia from the year 1982 to 2013. The result shows that the interval length and the order of FTS will affect the forecasting accuracy. Moreover, the similarity measure concept can be used as an alternative approach to measure the accuracy without going through the defuzzification process.
format	Thesis
qualification_level	Master's degree
author	Ab Mutalib, Siti Musleha
spellingShingle	Ab Mutalib, Siti Musleha Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib
author_facet	Ab Mutalib, Siti Musleha
author_sort	Ab Mutalib, Siti Musleha
title	Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib
title_short	Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib
title_full	Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib
title_fullStr	Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib
title_full_unstemmed	Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib
title_sort	fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / siti musleha ab mutalib
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/89135/1/89135.pdf
_version_	1804889802035691520

Fuzzy time series forecasting model based on trapezoidal fuzzy numbers and similarity measure approach / Siti Musleha Ab Mutalib

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