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Time‐series analysis supported by power transformations
Author(s) -
Guerrero Victor M.
Publication year - 1993
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.3980120104
Subject(s) - series (stratigraphy) , computer science , transformation (genetics) , variance (accounting) , scale (ratio) , time series , algorithm , econometrics , mathematics , machine learning , paleontology , biochemistry , chemistry , physics , accounting , quantum mechanics , biology , business , gene
This paper presents some procedures aimed at helping an applied time‐series analyst in the use of power transformations. Two methods are proposed for selecting a variance‐stabilizing transformation and another for bias‐reduction of the forecast in the original scale. Since these methods are essentially model‐independent, they can be employed with practically any type of time‐series model. Some comparisons are made with other methods currently available and it is shown that those proposed here are either easier to apply or are more general, with a performance similar to or better than other competing procedures.