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Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation
Author(s) -
Yang ZhengLing,
Liu YaDi,
Zhu XinShan,
Chen Xi,
Zhang Jun
Publication year - 2016
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2407
Subject(s) - white noise , square root , transformation (genetics) , gaussian , gaussian noise , taylor series , additive white gaussian noise , noise (video) , mean squared error , mathematics , series (stratigraphy) , root mean square , square (algebra) , mean square , variance (accounting) , statistics , statistical physics , computer science , mathematical analysis , algorithm , physics , artificial intelligence , geology , geometry , business , image (mathematics) , chemistry , paleontology , biochemistry , accounting , quantum mechanics , gene
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.