Dendritic neuron model neural network trained by modified particle swarm optimization for time-series forecasting


Yilmaz A., Yolcu U.

JOURNAL OF FORECASTING, 2021 (Journal Indexed in SSCI) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2021
  • Doi Number: 10.1002/for.2833
  • Title of Journal : JOURNAL OF FORECASTING
  • Keywords: dendritic neuron model, forecasting, modified particle swarm optimization, TAIEX, time-series, ANFIS, PREDICTION, REGRESSION

Abstract

Different types of artificial neural networks (NNs), such as nonprobabilistic and computation-based time-series forecasting tools, are widely and successfully used in the time-series literature. Whereas some of them use an additive aggregation function, others use a multiplicative aggregation function in the structure of their neuron models. In particular, recently proposed sigma-pi NNs and dendritic NNs have additional and multiplicative neuron models. This study aims to take advantage of the dendritic neuron model neural network (DNM-NN) in forecasting and hence uses the DNM-NN trained by a modified particle swarm optimization as the main contribution of the study optimization in time-series forecasting to improve the forecasting accuracy. To evaluate the forecasting performance of the DNM-NN, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) was analyzed, and the obtained results were discussed together with the results produced by other time-series forecasting models, including traditional, fuzzy-based, and computational-based models.