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

Abstract

AbstractDifferent 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.