Home > Research > Publications & Outputs > Forecasting nonlinear time series with a hybrid...

Links

Text available via DOI:

View graph of relations

Forecasting nonlinear time series with a hybrid methodology

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Forecasting nonlinear time series with a hybrid methodology. / Aladag, Cagdas Hakan; Egrioglu, Erol; Kadilar, Cem.
In: Applied Mathematics Letters, Vol. 22, No. 9, 01.09.2009, p. 1467-1470.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Aladag, CH, Egrioglu, E & Kadilar, C 2009, 'Forecasting nonlinear time series with a hybrid methodology', Applied Mathematics Letters, vol. 22, no. 9, pp. 1467-1470. https://doi.org/10.1016/j.aml.2009.02.006

APA

Aladag, C. H., Egrioglu, E., & Kadilar, C. (2009). Forecasting nonlinear time series with a hybrid methodology. Applied Mathematics Letters, 22(9), 1467-1470. https://doi.org/10.1016/j.aml.2009.02.006

Vancouver

Aladag CH, Egrioglu E, Kadilar C. Forecasting nonlinear time series with a hybrid methodology. Applied Mathematics Letters. 2009 Sept 1;22(9):1467-1470. doi: 10.1016/j.aml.2009.02.006

Author

Aladag, Cagdas Hakan ; Egrioglu, Erol ; Kadilar, Cem. / Forecasting nonlinear time series with a hybrid methodology. In: Applied Mathematics Letters. 2009 ; Vol. 22, No. 9. pp. 1467-1470.

Bibtex

@article{d19b02d89d1a4cb8aeb5d8c9763c1c09,
title = "Forecasting nonlinear time series with a hybrid methodology",
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.",
keywords = "ARIMA, Canadian lynx data, Hybrid method, Recurrent neural networks, Time series forecasting",
author = "Aladag, {Cagdas Hakan} and Erol Egrioglu and Cem Kadilar",
year = "2009",
month = sep,
day = "1",
doi = "10.1016/j.aml.2009.02.006",
language = "English",
volume = "22",
pages = "1467--1470",
journal = "Applied Mathematics Letters",
issn = "0893-9659",
publisher = "Elsevier Limited",
number = "9",

}

RIS

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 -