Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship

In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anyth...

Full description

Bibliographic Details
Main Authors: Wangren Qiu, Ping Zhang, Yanhong Wang
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/962597
id doaj-b62ef28b2ff144e194567d1438186ec0
record_format Article
spelling doaj-b62ef28b2ff144e194567d1438186ec02020-11-24T21:06:09ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/962597962597Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical RelationshipWangren Qiu0Ping Zhang1Yanhong Wang2Department of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333001, ChinaDepartment of Economics, Hefei University of Technology, Hefei 230009, ChinaDepartment of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333001, ChinaIn view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS (M, N) based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parameters M and N, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.http://dx.doi.org/10.1155/2015/962597
collection DOAJ
language English
format Article
sources DOAJ
author Wangren Qiu
Ping Zhang
Yanhong Wang
spellingShingle Wangren Qiu
Ping Zhang
Yanhong Wang
Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship
Mathematical Problems in Engineering
author_facet Wangren Qiu
Ping Zhang
Yanhong Wang
author_sort Wangren Qiu
title Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship
title_short Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship
title_full Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship
title_fullStr Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship
title_full_unstemmed Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship
title_sort fuzzy time series forecasting model based on automatic clustering techniques and generalized fuzzy logical relationship
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS (M, N) based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parameters M and N, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.
url http://dx.doi.org/10.1155/2015/962597
work_keys_str_mv AT wangrenqiu fuzzytimeseriesforecastingmodelbasedonautomaticclusteringtechniquesandgeneralizedfuzzylogicalrelationship
AT pingzhang fuzzytimeseriesforecastingmodelbasedonautomaticclusteringtechniquesandgeneralizedfuzzylogicalrelationship
AT yanhongwang fuzzytimeseriesforecastingmodelbasedonautomaticclusteringtechniquesandgeneralizedfuzzylogicalrelationship
_version_ 1716766642676957184