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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 06/01/2021, available online: https://www.tandfonline.com/doi/abs/10.1080/02664763.2020.1869702

    Accepted author manuscript, 413 KB, PDF document

    Embargo ends: 6/01/22

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Threshold single multiplicative neuron artificial neural networks for non-linear time series forecasting

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
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<mark>Journal publication date</mark>6/01/2021
<mark>Journal</mark>Journal of Applied Statistics
Number of pages17
Publication StatusE-pub ahead of print
Early online date6/01/21
<mark>Original language</mark>English

Abstract

Single multiplicative neuron artificial neural networks have different importance than many other artificial neural networks because they do not have complex architecture problem, too many parameters and they need more computation time to use. In single multiplicative neuron artificial neural network, it is assumed that there is a one data generation process for time series. Many time series need an assumption that they have two data generation process or more. Based on this idea, the threshold model structure can be employed in a single multiplicative neuron model artificial neural network for taking into considering data generation processes problem. In this study, a new artificial neural network type is proposed and it is called a threshold single multiplicative neuron artificial neural network. It is assumed that time series have two data generation processes according to the architecture of single multiplicative neuron artificial neural network. Training algorithms are proposed based on harmony search algorithm and particle swarm optimization for threshold single multiplicative neuron artificial neural network. The proposed method is tested by various time series data sets and compared with well-known forecasting methods by considering different error measures. Finally, the performance of the proposed method is evaluated by a simulation study.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 06/01/2021, available online: https://www.tandfonline.com/doi/abs/10.1080/02664763.2020.1869702