Forecast combination by using artificial neural networks

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Forecast combination by using artificial neural networks. / Aladag, Cagdas Hakan; Egrioglu, Erol; Yolcu, Ufuk.
In: Neural Processing Letters, Vol. 32, No. 3, 01.12.2010, p. 269-276.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Aladag, CH, Egrioglu, E & Yolcu, U 2010, 'Forecast combination by using artificial neural networks', Neural Processing Letters, vol. 32, no. 3, pp. 269-276. https://doi.org/10.1007/s11063-010-9156-7

APA

Aladag, C. H., Egrioglu, E., & Yolcu, U. (2010). Forecast combination by using artificial neural networks. Neural Processing Letters, 32(3), 269-276. https://doi.org/10.1007/s11063-010-9156-7

Vancouver

Aladag CH, Egrioglu E, Yolcu U. Forecast combination by using artificial neural networks. Neural Processing Letters. 2010 Dec 1;32(3):269-276. Epub 2010 Oct 30. doi: 10.1007/s11063-010-9156-7

Author

Aladag, Cagdas Hakan ; Egrioglu, Erol ; Yolcu, Ufuk. / Forecast combination by using artificial neural networks. In: Neural Processing Letters. 2010 ; Vol. 32, No. 3. pp. 269-276.

Bibtex

@article{6a336e6b465741c48745591424fc8ee0,

title = "Forecast combination by using artificial neural networks",

abstract = "One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.",

keywords = "Artificial neural networks, Forecast combination, Forecasting, Fuzzy time series",

author = "Aladag, {Cagdas Hakan} and Erol Egrioglu and Ufuk Yolcu",

year = "2010",

month = dec,

day = "1",

doi = "10.1007/s11063-010-9156-7",

language = "English",

volume = "32",

pages = "269--276",

journal = "Neural Processing Letters",

issn = "1370-4621",

publisher = "Springer",

number = "3",

}

RIS

TY - JOUR

T1 - Forecast combination by using artificial neural networks

AU - Aladag, Cagdas Hakan

AU - Egrioglu, Erol

AU - Yolcu, Ufuk

PY - 2010/12/1

Y1 - 2010/12/1

N2 - One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.

AB - One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.

KW - Artificial neural networks

KW - Forecast combination

KW - Forecasting

KW - Fuzzy time series

U2 - 10.1007/s11063-010-9156-7

DO - 10.1007/s11063-010-9156-7

M3 - Journal article

AN - SCOPUS:78650693344

VL - 32

SP - 269

EP - 276

JO - Neural Processing Letters

JF - Neural Processing Letters

SN - 1370-4621

IS - 3

ER -

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