eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2019-01-01 7 1 25 31 10.12691/ajams-7-1-4 AJAMS2019714 article WMND Basnayake nadeeka@stat.cmb.ac.lk 1 MDT Attygalle 1 Liwan Liyanage-Hansen 2 KDW Nandalal 3 Department of Statistics, University of Colombo, Colombo, Sri Lanka School of Computing, Engineering and Mathematics, Western Sydney University, Western Sydney, Australia Department of Civil Engineering, University of Peradeniya, Peradeniya, Sri Lanka In spite of the ability of Arti?cial Neural Network (ANN) to handle nonlinear relationships in data, there are instances where ANNs have not been able to predict accurately in the presence of non-stationarity. A novel algorithm that has the ability to treat the nonstationary and nonlinearity in a time series had been presented in [1]. This paper presents a modification done to the algorithm via addressing the edge distortion that arises in the real time execution. The proposed algorithm in [1] was named as '1D Multilevel DWT Segmented ANN Algorithm' where the modified algorithm presented in this paper will be called as 'Denoised 1D Multilevel DWT Segmented ANN Algorithm'. http://pubs.sciepub.com/ajams/7/1/4/ajams-7-1-4.pdf edge distortion nonlinear non-stationary wavelet NAR-ANN