Fuzzy lagged variable selection in fuzzy time series with genetic algorithms |
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| Affiliation: | 1. Department of Statistics, Hacettepe University, Ankara 06800, Turkey;2. Department of Statistics, Ondokuz Mayis University, Samsun 55139, Turkey;1. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, B-9000 Gent, Belgium;2. Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, B-9000 Gent, Belgium;1. Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China;2. Institute of Clinical Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China;3. Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China;1. School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea;2. Department of Electrical Electronic and Control Engineering, Hankyong National University, Sukjong-dong, Ansung-si, Gyunggi-do 456-749, Republic of Korea;1. Department of Industrial Power, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal Melaka, Malaysia;2. Center for Industrial and Applied Mathematics, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia;3. Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia;1. School of Automation, Beijing Institute of Technology, Beijing, China;2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong |
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| Abstract: | Fuzzy time series forecasting models can be divided into two subclasses which are first order and high order. In high order models, all lagged variables exist in the model according to the model order. Thus, some of these can exist in the model although these lagged variables are not significant in explaining fuzzy relationships. If such lagged variables can be removed from the model, fuzzy relationships will be defined better and it will cause more accurate forecasting results. In this study, a new fuzzy time series forecasting model has been proposed by defining a partial high order fuzzy time series forecasting model in which the selection of fuzzy lagged variables is done by using genetic algorithms. The proposed method is applied to some real life time series and obtained results are compared with those obtained from other methods available in the literature. It is shown that the proposed method has high forecasting accuracy. |
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| Keywords: | Forecasting Fuzzy time series Genetic algorithms Partial high order model Variable selection |
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