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A modified genetic algorithm for forecasting fuzzy time series

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Abstract

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.

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Acknowledgments

The authors would like to thank the anonymous reviewers and editors of journal for their valuable comments and suggestions to improve the quality of the paper.

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Authors and Affiliations

  1. Faculty of Arts and Science, Department of Statistics, Giresun University, Giresun, 28100, Turkey

    Eren Bas

  2. Faculty of Arts and Science, Department of Statistics, Ondokuz Mayıs University, Samsun, 55139, Turkey

    Vedide Rezan Uslu & Erol Egrioglu

  3. Science Faculty, Department of Statistics, Ankara University, Ankara, 06100, Turkey

    Ufuk Yolcu

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  1. Eren Bas
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  2. Vedide Rezan Uslu
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  3. Ufuk Yolcu
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  4. Erol Egrioglu
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Correspondence to Eren Bas.

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Bas, E., Uslu, V.R., Yolcu, U. et al. A modified genetic algorithm for forecasting fuzzy time series. Appl Intell 41, 453–463 (2014). https://doi.org/10.1007/s10489-014-0529-x

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