A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering
Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecastin...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2013/494239 |
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| _version_ | 1850160060157657088 |
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| author | Kamal S. Selim Gihan A. Elanany |
| author_facet | Kamal S. Selim Gihan A. Elanany |
| author_sort | Kamal S. Selim |
| collection | DOAJ |
| description | Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are still open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy of FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity and improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy clustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using these memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant researches. |
| format | Article |
| id | doaj-art-af6bdd9afe7441c0a35d0a8d6e99fae5 |
| institution | OA Journals |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-af6bdd9afe7441c0a35d0a8d6e99fae52025-08-20T02:23:16ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2013-01-01201310.1155/2013/494239494239A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy ClusteringKamal S. Selim0Gihan A. Elanany1Faculty of Economics and Political Science, Cairo University, EgyptSadat Academy for Management Sciences, Cairo, EgyptForecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are still open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy of FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity and improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy clustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using these memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant researches.http://dx.doi.org/10.1155/2013/494239 |
| spellingShingle | Kamal S. Selim Gihan A. Elanany A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering Advances in Fuzzy Systems |
| title | A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering |
| title_full | A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering |
| title_fullStr | A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering |
| title_full_unstemmed | A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering |
| title_short | A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering |
| title_sort | new method for short multivariate fuzzy time series based on genetic algorithm and fuzzy clustering |
| url | http://dx.doi.org/10.1155/2013/494239 |
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