Abstract
Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and the absolute mean square error were also used to measure the accuracy of the estimation for methods used. The important result obtained in this paper is that the optimal neural network was the Backpropagation (BP) and Recurrent neural networks (RNN) to solve time series, whether linear, semilinear, or non-linear. Besides, the result proved that the inefficiency and inaccuracy (failure) of RBF in solving nonlinear time series. However, RBF shows good efficiency in the case of linear or semi-linear time series only. It overcomes the problem of local minimum. The results showed improvements in the modern methods for time series forecasting.
Highlights
Time series forecasting is a very powerful computational method that allows predicting, future outcomes of a system based on how the system previously acted, it has a wide range of applications in many scientific fields
The results reveal that BP and ENN are better than general regression neural network (GRNN)
The results show that inefficiency or inability of GRNN spatially, Radial Basis Function Neural networks (RBF) generally to solve nonlinear time series and their efficiency to solve linear time series because it did not include feedback
Summary
Time series forecasting is a very powerful computational method that allows predicting, future outcomes of a system based on how the system previously acted, it has a wide range of applications in many scientific fields. Using neural networks and machine learning approaches can predict a wide range of different events, both of natural and human origin produced. Artificial neural networks (ANN) have received much attention in recent years, and the question of which type of neural networks are better in prediction has yet to be resolved. In statistics, forecasting the inputs for neural networks are typically the past observations of the info series and the output is that the future value. Most lag structures are to manage the number of input units of the corresponding neural networks. We must use techniques to avoid these problems[1]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
