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Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation

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  • Zheng‐Ling Yang
  • Ya‐Di Liu
  • Xin‐Shan Zhu
  • Xi Chen
  • Jun Zhang

Abstract

An analytical model has been developed in the present paper based on a square root transformation of white Gaussian noise. The mathematical expectation and variance of the new asymmetric distribution generated by white Gaussian noise after a square root transformation are analytically deduced from the preceding four terms of the Taylor expansion. The model was first evaluated against numerical experiments and a good agreement was obtained. The model was then used to predict time series of wind speeds and highway traffic flows. The simulation results from the new model indicate that the prediction accuracy could be improved by 0.1–1% by removing the mean errors. Further improvement could be obtained for non‐stationary time series, which had large trends. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Zheng‐Ling Yang & Ya‐Di Liu & Xin‐Shan Zhu & Xi Chen & Jun Zhang, 2016. "Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(8), pages 741-750, December.
    • Handle: RePEc:wly:jforec:v:35:y:2016:i:8:p:741-750
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    Cited by:

    1. Zi‐yu Chen & Fei Xiao & Xiao‐kang Wang & Min‐hui Deng & Jian‐qiang Wang & Jun‐Bo Li, 2022. "Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1458-1482, November.

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