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A new multiplicative seasonal neural network model based on particle swarm optimization

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A new multiplicative seasonal neural network model based on particle swarm optimization. / Aladag, Cagdas Hakan; Yolcu, Ufuk; Egrioglu, Erol.
In: Neural Processing Letters, Vol. 37, No. 3, 01.06.2013, p. 251-262.

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

Harvard

Aladag, CH, Yolcu, U & Egrioglu, E 2013, 'A new multiplicative seasonal neural network model based on particle swarm optimization', Neural Processing Letters, vol. 37, no. 3, pp. 251-262. https://doi.org/10.1007/s11063-012-9244-y

APA

Aladag, C. H., Yolcu, U., & Egrioglu, E. (2013). A new multiplicative seasonal neural network model based on particle swarm optimization. Neural Processing Letters, 37(3), 251-262. https://doi.org/10.1007/s11063-012-9244-y

Vancouver

Aladag CH, Yolcu U, Egrioglu E. A new multiplicative seasonal neural network model based on particle swarm optimization. Neural Processing Letters. 2013 Jun 1;37(3):251-262. doi: 10.1007/s11063-012-9244-y

Author

Aladag, Cagdas Hakan ; Yolcu, Ufuk ; Egrioglu, Erol. / A new multiplicative seasonal neural network model based on particle swarm optimization. In: Neural Processing Letters. 2013 ; Vol. 37, No. 3. pp. 251-262.

Bibtex

@article{8b53bec2217d419aa87e91e59abc9811,
title = "A new multiplicative seasonal neural network model based on particle swarm optimization",
abstract = "In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative seasonal ANN model is proposed to improve forecasting accuracy when time series with both trend and seasonal patterns is forecasted. This neural networks model suggested in this study is the first model proposed in the literature to model time series which contain both trend and seasonal variations. In the proposed approach, the defined neural network model is trained by particle swarm optimization. In the training process, local minimum traps are avoided by using this population based heuristic optimization method. The performance of the proposed approach is examined by using two real seasonal time series. The forecasts obtained from the proposed method are compared to those obtained from other forecasting techniques available in the literature. It is seen that the proposed forecasting model provides high forecasting accuracy.",
keywords = "Feed forward neural networks, Forecasting, Multiplicative neuron model, Particle swarm optimization, Time series, Training algorithm",
author = "Aladag, {Cagdas Hakan} and Ufuk Yolcu and Erol Egrioglu",
year = "2013",
month = jun,
day = "1",
doi = "10.1007/s11063-012-9244-y",
language = "English",
volume = "37",
pages = "251--262",
journal = "Neural Processing Letters",
issn = "1370-4621",
publisher = "Springer",
number = "3",

}

RIS

TY - JOUR

T1 - A new multiplicative seasonal neural network model based on particle swarm optimization

AU - Aladag, Cagdas Hakan

AU - Yolcu, Ufuk

AU - Egrioglu, Erol

PY - 2013/6/1

Y1 - 2013/6/1

N2 - In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative seasonal ANN model is proposed to improve forecasting accuracy when time series with both trend and seasonal patterns is forecasted. This neural networks model suggested in this study is the first model proposed in the literature to model time series which contain both trend and seasonal variations. In the proposed approach, the defined neural network model is trained by particle swarm optimization. In the training process, local minimum traps are avoided by using this population based heuristic optimization method. The performance of the proposed approach is examined by using two real seasonal time series. The forecasts obtained from the proposed method are compared to those obtained from other forecasting techniques available in the literature. It is seen that the proposed forecasting model provides high forecasting accuracy.

AB - In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative seasonal ANN model is proposed to improve forecasting accuracy when time series with both trend and seasonal patterns is forecasted. This neural networks model suggested in this study is the first model proposed in the literature to model time series which contain both trend and seasonal variations. In the proposed approach, the defined neural network model is trained by particle swarm optimization. In the training process, local minimum traps are avoided by using this population based heuristic optimization method. The performance of the proposed approach is examined by using two real seasonal time series. The forecasts obtained from the proposed method are compared to those obtained from other forecasting techniques available in the literature. It is seen that the proposed forecasting model provides high forecasting accuracy.

KW - Feed forward neural networks

KW - Forecasting

KW - Multiplicative neuron model

KW - Particle swarm optimization

KW - Time series

KW - Training algorithm

U2 - 10.1007/s11063-012-9244-y

DO - 10.1007/s11063-012-9244-y

M3 - Journal article

AN - SCOPUS:84878128285

VL - 37

SP - 251

EP - 262

JO - Neural Processing Letters

JF - Neural Processing Letters

SN - 1370-4621

IS - 3

ER -