An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
A tabu based neural network learning algorithm
Neurocomputing
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Expert Systems with Applications: An International Journal
A new approach based on artificial neural networks for high order multivariate fuzzy time series
Expert Systems with Applications: An International Journal
The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
Algorithm learning based neural network integrating feature selection and classification
Expert Systems with Applications: An International Journal
A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization
Neural Processing Letters
Hi-index | 12.05 |
Artificial neural networks (ANN) have been used in various applications in recent years. One of these applications is time series forecasting. Although ANN produces accurate forecasts in many time series implementations, there are still some problems with using ANN. ANN consist of some components such as architecture structure, learning algorithm and activation function. These components have important effect on the performance of ANN. An important decision is the selection of architecture structure that consists of determining the numbers of neurons in the layers of a network. Therefore, various approaches have been proposed to determine the best ANN architecture in the literature. However, the most preferred method is still trial and error method for finding a good architecture. In this study, a new architecture selection method based on tabu search algorithm is proposed. In the implementation, five real time series are analyzed by using ANN and the proposed method is employed to select the best architecture. For the comparison, these time series are also forecasted by using ANN when trial and error method is utilized to determine the best architecture. As a result of the implementation, it is clearly seen that better results are obtained when the proposed method is used for the selection of architecture.