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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Forecasting nonlinear time series with a hybrid methodology
AU - Aladag, Cagdas Hakan
AU - Egrioglu, Erol
AU - Kadilar, Cem
PY - 2009/9/1
Y1 - 2009/9/1
N2 - 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.
AB - 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.
KW - ARIMA
KW - Canadian lynx data
KW - Hybrid method
KW - Recurrent neural networks
KW - Time series forecasting
U2 - 10.1016/j.aml.2009.02.006
DO - 10.1016/j.aml.2009.02.006
M3 - Journal article
AN - SCOPUS:67349285333
VL - 22
SP - 1467
EP - 1470
JO - Applied Mathematics Letters
JF - Applied Mathematics Letters
SN - 0893-9659
IS - 9
ER -