Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

作     者：W.A.Shaikh S.F.Shah S.M.Pandhiani M.A.Solangi 

作者机构：Department of Mathematics and StatisticsQuaid-e-Awam University of EngineeringScience&TechnologyNawabshahSindhPakistan Department of Basic Sciences&Related StudiesMehran University of Engineering&TechnologyJamshoroSindhPakistan Department of General StudiesJubail University CollegeAl JubailSaudi Arabia 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2022年第130卷第3期

页      面：1517-1532页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Department of Basic Sciences & Related Studies Mehran University of Engineering and Technology, MUET 

主　　题：Impact wavelet decomposition combined traditional forecasting models statistical analysis 

摘      要：This investigative study is focused on the impact of wavelet on traditional forecasting time-series models,which significantly shows the usage of wavelet *** Decomposition(WD)algorithm has been combined with various traditional forecasting time-series models,such as Least Square Support Vector Machine(LSSVM),Artificial Neural Network(ANN)and Multivariate Adaptive Regression Splines(MARS)and their effects are examined in terms of the statistical *** WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters,which has yielded tremendous constructive ***,it is observed that the wavelet combined models are classy compared to the various time series models in terms of performance ***,combining wavelet forecasting models has yielded much better results.