Dendritic neuron model neural network trained by modified particle swarm optimization for time-series forecasting
JOURNAL OF FORECASTING, vol.41, no.4, pp.793-809, 2022 (SSCI, Scopus)
- Publication Type: Article / Article
- Volume: 41 Issue: 4
- Publication Date: 2022
- Doi Number: 10.1002/for.2833
- Journal Name: JOURNAL OF FORECASTING
- Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, Periodicals Index Online, ABI/INFORM, Business Source Elite, Business Source Premier, Compendex, EconLit, INSPEC, Public Affairs Index, vLex, zbMATH
- Page Numbers: pp.793-809
- Keywords: dendritic neuron model, forecasting, modified particle swarm optimization, TAIEX, time-series, ANFIS, PREDICTION, REGRESSION
- Ondokuz Mayıs University Affiliated: Yes
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.