Open Access

ARTICLE

Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

W. A. Shaikh^1,2,*, S. F. Shah², S. M. Pandhiani³, M. A. Solangi²

1 Department of Mathematics and Statistics, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, Sindh, Pakistan
2 Department of Basic Sciences & Related Studies, Mehran University of Engineering & Technology, Jamshoro, Sindh, Pakistan
3 Department of General Studies, Jubail University College, Al Jubail, Saudi Arabia

* Corresponding Author: W. A. Shaikh. Email:

(This article belongs to this Special Issue: New Trends in Statistical Computing and Data Science)

Computer Modeling in Engineering & Sciences 2022, 130(3), 1517-1532. https://doi.org/10.32604/cmes.2022.017822

Received 09 June 2021; Accepted 10 September 2021; Issue published 30 December 2021

Abstract

This investigative study is focused on the impact of wavelet on traditional forecasting time-series models, which significantly shows the usage of wavelet algorithms. 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 estimations. The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters, which has yielded tremendous constructive outcomes. Further, it is observed that the wavelet combined models are classy compared to the various time series models in terms of performance basis. Therefore, combining wavelet forecasting models has yielded much better results.

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Keywords

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