Forecasting nonlinear time series with a hybrid methodology

Aladag, Cagdas Hakan and Egrioglu, Erol and Kadilar, Cem (2009) Forecasting nonlinear time series with a hybrid methodology. Applied Mathematics Letters, 22 (9). pp. 1467-1470. ISSN 0893-9659

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Abstract

In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible to model both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybrid approach combining Elman's Recurrent Neural Networks (ERNN) and ARIMA models is proposed. The proposed hybrid approach is applied to Canadian Lynx data and it is found that the proposed approach has the best forecasting accuracy.

Item Type:
Journal Article
Journal or Publication Title:
Applied Mathematics Letters
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
ID Code:
139565
Deposited By:
Deposited On:
13 Dec 2019 15:40
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Mar 2020 09:04