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A new hybrid method for time series forecasting: AR–ANFIS

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Abstract

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Arts and Science, Marmara University, 34722, Istanbul, Turkey

    Busenur Sarıca

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

    Erol Eğrioğlu

  3. Department of Statistics, Faculty of Science and Letters, Mimar Sinan Fine Arts University, 34380, Istanbul, Turkey

    Barış Aşıkgil

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  1. Busenur Sarıca
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  2. Erol Eğrioğlu
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  3. Barış Aşıkgil
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Correspondence to Busenur Sarıca.

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Sarıca, B., Eğrioğlu, E. & Aşıkgil, B. A new hybrid method for time series forecasting: AR–ANFIS. Neural Comput & Applic 29, 749–760 (2018). https://doi.org/10.1007/s00521-016-2475-5

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  • DOI: https://doi.org/10.1007/s00521-016-2475-5

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