Vol.130, No.3, 2022, pp.1517-1532, doi:10.32604/cmes.2022.017822
OPEN ACCESS
ARTICLE
Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models
  • W. A. Shaikh1,2,*, 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)
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.
Keywords
Impact; wavelet decomposition; combined; traditional forecasting models; statistical analysis
Cite This Article
Shaikh, W. A., Shah, S. F., Pandhiani, S. M., Solangi, M. A. (2022). Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models. CMES-Computer Modeling in Engineering & Sciences, 130(3), 1517–1532.
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