Forecasting nonlinear time series with a hybrid methodology

Applied Mathematics Letters (Impact Factor: 1.34). 09/2009; 22(9):1467-1470. DOI: 10.1016/j.aml.2009.02.006
Source: DBLP


a b s t r a c t 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.

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    • "Then, Luna et al. [21], Rojas et al. [22], Wong et al. [23], and Zhao and Yang [24] used the fuzzy clustering and artificial neural network (ANN) to solve the problem of time series forecasting. Aladag et al. [25] proposed a new hybrid approach by combining Elman's recurrent neural networks (ERNN) and ARIMA models to forecast the nonlinear times series like the Canadian Lynx data. Egrioglu et al. [26] constructed a hybrid model of seasonal ARIMA, autoregressive conditional heteroscedasticity (ARCH), and ANN to forecast nonlinear time series. "
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