Improved models in fuzzy time series for forecasting

The focus of this research is in the area of fuzzy time series. Such a study is important in order to improve the forecasting performance. The research approach adopted in this thesis includes introducing polynomial fuzzy time series, differential fuzzy logic relationships model, multi-layer stock f...

Full description

Saved in:
Bibliographic Details
Main Author: Sadaei, Hossein Javedani
Format: Thesis
Language:English
Published: 2013
Subjects:
Online Access:http://eprints.utm.my/id/eprint/34630/5/HosseinJavedaniSadaeiPFS2013.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-utm-ep.34630
record_format uketd_dc
spelling my-utm-ep.346302021-06-15T14:05:19Z Improved models in fuzzy time series for forecasting 2013-06 Sadaei, Hossein Javedani QA Mathematics The focus of this research is in the area of fuzzy time series. Such a study is important in order to improve the forecasting performance. The research approach adopted in this thesis includes introducing polynomial fuzzy time series, differential fuzzy logic relationships model, multi-layer stock forecasting model, data pre-processing approach, and k-step-ahead forecasting. The findings from this research provide evidence that integration of the polynomial concept and non- linear optimization transfer the fuzzy time series to a parametric model. By using polynomial fuzzy time series, 83% of experiments were improved significantly. Differential fuzzy logical relationships were defined to be used for establishing differential fuzzy logical relationship groups. By utilizing differential fuzzy time series in Taiwan Capitalization Weighted Stock Index (TAIEX) datasets, 90% of the results were improved and as for enrollment datasets this statistic was 100%. Data pre-processing approach managed to reduce the negative effects of noisy data by transforming the data into a new domain. By applying integrated data pre-processing fuzzy time series algorithm to short term load data and TAIEX, the average of Mean Absolute Percentage Errors (MAPEs) and Root Mean Square Errors (RMSEs) were reduced by 12.05 and 1.98, respectively. The multi-layer forecasting model enhances the performance of stock forecast values. Many experiments that were carried out on the forty years' stock data indicated that multi-layer fuzzy time series model could be considered as an advanced model for stock market forecasting. The one-day ahead forecasting was successfully employed to England and France 2006 half-hourly load data. The main conclusion drawn from this study suggests that the proposed methods were accurate compared to their counterparts. In addition, the functionality of the proposed methods was enhanced through the proposed algorithms which were tested to be robust and reliable. All of these findings were confirmed through various tests of the proposed methods on numerous case studies. The thesis also recommends that the fuzzy time series model should be considered in forecasting alongside with classical approaches. 2013-06 Thesis http://eprints.utm.my/id/eprint/34630/ http://eprints.utm.my/id/eprint/34630/5/HosseinJavedaniSadaeiPFS2013.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
language English
topic QA Mathematics
spellingShingle QA Mathematics
Sadaei, Hossein Javedani
Improved models in fuzzy time series for forecasting
description The focus of this research is in the area of fuzzy time series. Such a study is important in order to improve the forecasting performance. The research approach adopted in this thesis includes introducing polynomial fuzzy time series, differential fuzzy logic relationships model, multi-layer stock forecasting model, data pre-processing approach, and k-step-ahead forecasting. The findings from this research provide evidence that integration of the polynomial concept and non- linear optimization transfer the fuzzy time series to a parametric model. By using polynomial fuzzy time series, 83% of experiments were improved significantly. Differential fuzzy logical relationships were defined to be used for establishing differential fuzzy logical relationship groups. By utilizing differential fuzzy time series in Taiwan Capitalization Weighted Stock Index (TAIEX) datasets, 90% of the results were improved and as for enrollment datasets this statistic was 100%. Data pre-processing approach managed to reduce the negative effects of noisy data by transforming the data into a new domain. By applying integrated data pre-processing fuzzy time series algorithm to short term load data and TAIEX, the average of Mean Absolute Percentage Errors (MAPEs) and Root Mean Square Errors (RMSEs) were reduced by 12.05 and 1.98, respectively. The multi-layer forecasting model enhances the performance of stock forecast values. Many experiments that were carried out on the forty years' stock data indicated that multi-layer fuzzy time series model could be considered as an advanced model for stock market forecasting. The one-day ahead forecasting was successfully employed to England and France 2006 half-hourly load data. The main conclusion drawn from this study suggests that the proposed methods were accurate compared to their counterparts. In addition, the functionality of the proposed methods was enhanced through the proposed algorithms which were tested to be robust and reliable. All of these findings were confirmed through various tests of the proposed methods on numerous case studies. The thesis also recommends that the fuzzy time series model should be considered in forecasting alongside with classical approaches.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Sadaei, Hossein Javedani
author_facet Sadaei, Hossein Javedani
author_sort Sadaei, Hossein Javedani
title Improved models in fuzzy time series for forecasting
title_short Improved models in fuzzy time series for forecasting
title_full Improved models in fuzzy time series for forecasting
title_fullStr Improved models in fuzzy time series for forecasting
title_full_unstemmed Improved models in fuzzy time series for forecasting
title_sort improved models in fuzzy time series for forecasting
granting_institution Universiti Teknologi Malaysia, Faculty of Science
granting_department Faculty of Science
publishDate 2013
url http://eprints.utm.my/id/eprint/34630/5/HosseinJavedaniSadaeiPFS2013.pdf
_version_ 1747816221296820224