Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation

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.

Stochastic configuration network based on improved whale optimization algorithm for nonstationary time series prediction

“…A large number of scholars have explored data pre‐processing methods to address the nonstationarity, fluctuation, and randomness of time series (Bi et al, 2019; Yang et al, 2016). Liu, Mi, et al (2019) employed singular spectrum analysis to decompose time series.…”