Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

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• 作者：Erol Egrioglu (1)
  Ufuk Yolcu (2)
  Cagdas Hakan Aladag (3)
  Eren Bas (4)
  
  1. Department of Statistics ; Faculty of Arts and Science ; Ondokuz May谋s University ; 55139 ; 聽Samsun ; Turkey
  2. Department of Statistics ; Faculty of Science ; Ankara University ; 06100 ; 聽Ankara ; Turkey
  3. Department of Statistics ; Faculty of Science ; Hacettepe University ; 06200 ; 聽Ankara ; Turkey
  4. Department of Statistics ; Faculty of Arts and Science ; Giresun University ; 28000 ; 聽Giresun ; Turkey
  
• 关键词：Artificial neural networks ; Forecasting ; Multiplicative neuron model ; Non ; linear time series ; Recurrent neural networks
• 刊名：Neural Processing Letters
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：41
• 期：2
• 页码：249-258
• 全文大小：317 KB
• 参考文献：1. Aladag, CH (2011) A new architecture selection method based on tabu search for artificial neural networks. Expert Syst Appl 38: pp. 3287-3293 CrossRef
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  3. Aladag, CH, Egrioglu, E, Gunay, S, Basaran, MA (2010) Improving weighted information criterion by using optimization. J Comput Appl Math 233: pp. 2683-2687 CrossRef
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• 刊物类别：Physics and Astronomy
• 刊物主题：Physics
  Complexity
  Artificial Intelligence and Robotics
  Electronic and Computer Engineering
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1573-773X

文摘

Artificial neural networks (ANN) have been widely used in recent years to model non-linear time series since ANN approach is a responsive method and does not require some assumptions such as normality or linearity. An important problem with using ANN for time series forecasting is to determine the number of neurons in hidden layer. There have been some approaches in the literature to deal with the problem of determining the number of neurons in hidden layer. A new ANN model was suggested which is called multiplicative neuron model (MNM) in the literature. MNM has only one neuron in hidden layer. Therefore, the problem of determining the number of neurons in hidden layer is automatically solved when MNM is employed. Also, MNM can produce accurate forecasts for non-linear time series. ANN models utilized for non-linear time series have generally autoregressive structures since lagged variables of time series are generally inputs of these models. On the other hand, it is a well-known fact that better forecasts for real life time series can be obtained from models whose inputs are lagged variables of error. In this study, a new recurrent multiplicative neuron neural network model is firstly proposed. In the proposed method, lagged variables of error are included in the model. Also, the problem of determining the number of neurons in hidden layer is avoided when the proposed method is used. To train the proposed neural network model, particle swarm optimization algorithm was used. To evaluate the performance of the proposed model, it was applied to a real life time series. Then, results produced by the proposed method were compared to those obtained from other methods. It was observed that the proposed method has superior performance to existing methods.