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A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks

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

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A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks. / Aladag, Cagdas Hakan; Egrioglu, Erol; Gunay, Suleyman.
In: Hacettepe Journal of Mathematics and Statistics, Vol. 37, No. 2, 01.12.2008, p. 185-200.

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

Harvard

Aladag, CH, Egrioglu, E & Gunay, S 2008, 'A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks', Hacettepe Journal of Mathematics and Statistics, vol. 37, no. 2, pp. 185-200.

APA

Aladag, C. H., Egrioglu, E., & Gunay, S. (2008). A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks. Hacettepe Journal of Mathematics and Statistics, 37(2), 185-200.

Vancouver

Aladag CH, Egrioglu E, Gunay S. A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks. Hacettepe Journal of Mathematics and Statistics. 2008 Dec 1;37(2):185-200.

Author

Aladag, Cagdas Hakan ; Egrioglu, Erol ; Gunay, Suleyman. / A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks. In: Hacettepe Journal of Mathematics and Statistics. 2008 ; Vol. 37, No. 2. pp. 185-200.

Bibtex

@article{7d7abe800a8b43578f8601b979f40c50,
title = "A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks",
abstract = "The only suggestions given in the literature for determining the archi- tecture of neural networks are based on observations, and a simulation study to determine the architecture has not yet been reported. Based on the results of the simulation study described in this paper, a new architecture selection strategy is proposed and shown to work well. It is noted that although in some studies the period of a seasonal time series has been taken as the number of inputs of the neural network model, it is found in this study that the period of a seasonal time series is not a parameter in determining the number of inputs.",
keywords = "Architecture selection, Forecasting, Neural networks, Seasonal autoregressive time series, Simulation",
author = "Aladag, {Cagdas Hakan} and Erol Egrioglu and Suleyman Gunay",
year = "2008",
month = dec,
day = "1",
language = "English",
volume = "37",
pages = "185--200",
journal = "Hacettepe Journal of Mathematics and Statistics",
issn = "1303-5010",
publisher = "Hacettepe University",
number = "2",

}

RIS

TY - JOUR

T1 - A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks

AU - Aladag, Cagdas Hakan

AU - Egrioglu, Erol

AU - Gunay, Suleyman

PY - 2008/12/1

Y1 - 2008/12/1

N2 - The only suggestions given in the literature for determining the archi- tecture of neural networks are based on observations, and a simulation study to determine the architecture has not yet been reported. Based on the results of the simulation study described in this paper, a new architecture selection strategy is proposed and shown to work well. It is noted that although in some studies the period of a seasonal time series has been taken as the number of inputs of the neural network model, it is found in this study that the period of a seasonal time series is not a parameter in determining the number of inputs.

AB - The only suggestions given in the literature for determining the archi- tecture of neural networks are based on observations, and a simulation study to determine the architecture has not yet been reported. Based on the results of the simulation study described in this paper, a new architecture selection strategy is proposed and shown to work well. It is noted that although in some studies the period of a seasonal time series has been taken as the number of inputs of the neural network model, it is found in this study that the period of a seasonal time series is not a parameter in determining the number of inputs.

KW - Architecture selection

KW - Forecasting

KW - Neural networks

KW - Seasonal autoregressive time series

KW - Simulation

M3 - Journal article

AN - SCOPUS:77956270587

VL - 37

SP - 185

EP - 200

JO - Hacettepe Journal of Mathematics and Statistics

JF - Hacettepe Journal of Mathematics and Statistics

SN - 1303-5010

IS - 2

ER -