Recurrent dendritic neuron model artificial neural network for time series forecasting

Erol Egrioglu, Eren Baş, Mu Yen Chen

研究成果: Article同行評審

14 引文 斯高帕斯(Scopus)

摘要

Various neuron models have been proposed in the literature. Their structures are the simplest imitation for the biological neuron models. The dendritic neuron model is closer to biological neuron than the others. The single dendritic neuron model artificial neural networks have produced good forecasting results in the literature. In this study, a new recurrent dendritic neuron model artificial neural network is proposed. Moreover, a training algorithm based on particle swarm optimization is proposed. The performance of the proposed method is examined on SP500 stock exchange data sets. The proposed method produces better forecasts than long short term memory and Pi-Sigma artificial neural network. Moreover, the forecasting performance of the proposed method is investigated by using M3 and M4 competitions yearly data, totally used 23645-time series. According to application results, the proposed method produces very competitive results and it is the second-best method for M3 competition yearly data and it is the fifth-best method for M4 competition yearly data. Moreover, it is the best machine learning method for both M3 and M4 performance.

原文English
頁(從 - 到)572-584
頁數13
期刊Information sciences
607
DOIs
出版狀態Published - 2022 8月

All Science Journal Classification (ASJC) codes

  • 軟體
  • 控制與系統工程
  • 理論電腦科學
  • 電腦科學應用
  • 資訊系統與管理
  • 人工智慧

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