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Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling

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Abstract

Single multiplicative neuron model and multilayer perceptron have been commonly used for time series prediction problem. Having a simple structure and features of easily applicable differentiates the single multiplicative neuron model from the multilayer perception. While, multilayer perceptron just as many other artificial neural networks are data-based methods, single multiplicative neuron model has a model structure due to it is composed of a single neuron. Multilayer perceptron can highly compliance with data by changing its architecture, though single multiplicative neuron model, in this respect, is insufficient. In this study, to overcome this problem of single multiplicative neuron model, a new model that its weights and biases are obtained by way of autoregressive equations is proposed. Since the time indexes are considered to determine weights and biases from the autoregressive models, the proposed neural network can be evaluated as a data-based model. To show the performance and capability of the proposed method, various implementations have been executed over some well-known data sets. And the obtained results demonstrate that data-based proposed method has outstanding forecasting performance.

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Authors and Affiliations

  1. Department of Industrial Engineering, Forecast Research Laboratory, Giresun University, 28200, Giresun, Turkey

    Ozge Cagcag Yolcu

  2. Department of Statistics, Forecast Research Laboratory, Giresun University, 28200, Giresun, Turkey

    Eren Bas & Erol Egrioglu

  3. Department of Econometrics, Forecast Research Laboratory, Giresun University, 28200, Giresun, Turkey

    Ufuk Yolcu

Authors
  1. Ozge Cagcag Yolcu
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  2. Eren Bas
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  3. Erol Egrioglu
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  4. Ufuk Yolcu
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Correspondence to Ozge Cagcag Yolcu.

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Cagcag Yolcu, O., Bas, E., Egrioglu, E. et al. Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling. Neural Process Lett 47, 1133–1147 (2018). https://doi.org/10.1007/s11063-017-9686-3

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