Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
16 January 2020 |
31 January 2020 |
10 March 2020 |
Abstract: Wavelets are a class of functions such that multiple resolution nature of wavelets that provides a natural frame work for the analysis of time series. A wavelet network is an important tool for analyzing time series especially when it is nonlinear and non-stationary. It takes advantage of high resolution of wavelets and learning and feed forward nature of Neural Networks. The power of this network to approximate functions from given input-output data is proved and it has utilized the localization property of a wavelet to focus on local properties. Here we are analyzing the time series of of daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.
