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An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors

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Abstract

Fuzzy time series forecasting method has been applied in several domains, such as stock market price, temperature, sales, crop production and academic enrollments. In this paper, we introduce a model to deal with forecasting problems of two factors. The proposed model is designed using fuzzy time series and artificial neural network. In a fuzzy time series forecasting model, the length of intervals in the universe of discourse always affects the results of forecasting. Therefore, an artificial neural network- based technique is employed for determining the intervals of the historical time series data sets by clustering them into different groups. The historical time series data sets are then fuzzified, and the high-order fuzzy logical relationships are established among fuzzified values based on fuzzy time series method. The paper also introduces some rules for interval weighing to defuzzify the fuzzified time series data sets. From experimental results, it is observed that the proposed model exhibits higher accuracy than those of existing two-factors fuzzy time series models.

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Acknowledgments

We are thankful to Hasin A. Ahmed, Research Fellow of the Department of Computer Science and Engineering, Tezpur University, Tezpur (India), for encouragement, valuable suggestions and discussions. Constructive comments by two anonymous reviewers helped to improve the revised manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Tezpur University, Tezpur-28, India

    Pritpal Singh & Bhogeswar Borah

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  1. Pritpal Singh
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  2. Bhogeswar Borah
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Correspondence to Pritpal Singh.

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Singh, P., Borah, B. An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors. Knowl Inf Syst 38, 669–690 (2014). https://doi.org/10.1007/s10115-012-0603-9

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  • DOI: https://doi.org/10.1007/s10115-012-0603-9

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