A new method for short multivariate fuzzy time series based on genetic algorithm and fuzzy clustering
type: Item
instance of: scholarly article
title: A new method for short multivariate fuzzy time series based on genetic algorithm and fuzzy clustering
zbMATH Open document ID: 1323.62088
DOI: 10.1155/2013/494239
author: K. Selim
author: Gihan A. Elanany
published in: Advances in Fuzzy Systems
publication date: 2015-11-02T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62M86
zbMATH DE Number: 6502864
Wikidata QID: Q58918402
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/494239
OpenAlex ID: W2125250808
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description: scientific article; zbMATH DE number 6502864