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Type-1 fuzzy time series function method based on binary particle swarm optimisation

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<mark>Journal publication date</mark>31/01/2016
<mark>Journal</mark>International Journal of Data Analysis Techniques and Strategies
Issue number1
Volume8
Number of pages12
Pages (from-to)2-13
Publication StatusPublished
<mark>Original language</mark>English

Abstract

For time series forecasting four kinds of fuzzy-based approaches can be used. These are fuzzy regression techniques, fuzzy time series methods, fuzzy inference systems, and fuzzy function approaches. There are some major problems in using fuzzy regression techniques and fuzzy inference systems for time series forecasting. Therefore, it would be wise to use a forecasting approach which combines fuzzy time series and fuzzy function approaches. In this study, a fuzzy time series forecasting method based on fuzzy function approach is proposed by adopting fuzzy function approach to time series forecasting. And, the proposed approach is called type-1 fuzzy time series function approach. Also, in the proposed approach, the lagged variables of the system are determined by using binary particle swarm optimisation. In order to evaluate the performance of the proposed method, it has been applied to well-known time series of Australian beer consumption and Istanbul stock exchange dataset.