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Neural Computing and Applications

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21.07.2016 | Original Article

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

verfasst von: Busenur Sarıca, Erol Eğrioğlu, Barış Aşıkgil

Erschienen in: Neural Computing and Applications | Ausgabe 3/2018

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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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Titel: A new hybrid method for time series forecasting: AR–ANFIS
verfasst von: Busenur Sarıca
Erol Eğrioğlu
Barış Aşıkgil
Publikationsdatum: 21.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2475-5

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