Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves

In this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. An application algorithm was proposed to transform the positive original responses using the first model and the stationary response...

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Main Authors: Sameera Abdulsalam Othman, Haithem Taha Mohammed Ali
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Advances in Mathematical Physics
Online Access:http://dx.doi.org/10.1155/2021/6676400
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author Sameera Abdulsalam Othman
Haithem Taha Mohammed Ali
author_facet Sameera Abdulsalam Othman
Haithem Taha Mohammed Ali
author_sort Sameera Abdulsalam Othman
collection DOAJ
description In this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. An application algorithm was proposed to transform the positive original responses using the first model and the stationary responses using the second model to improve the nonparametric estimation of the functional time series. The Box-Cox model contributed to improving the results of the nonparametric estimation of the original data, but the results become somewhat confusing after attempting to make the transformed response variable stationary in the mean, while the functional time series predictions were more accurate using the transformed stationary datasets using the Yeo-Johnson model.
format Article
id doaj-art-ec5b7029de0949789056d501ff0a52d5
institution Kabale University
issn 1687-9120
1687-9139
language English
publishDate 2021-01-01
publisher Wiley
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series Advances in Mathematical Physics
spelling doaj-art-ec5b7029de0949789056d501ff0a52d52025-02-03T06:47:01ZengWileyAdvances in Mathematical Physics1687-91201687-91392021-01-01202110.1155/2021/66764006676400Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature CurvesSameera Abdulsalam Othman0Haithem Taha Mohammed Ali1Department of Mathematics, College of Basic Education, University of Duhok, Kurdistan Region, IraqDepartment of Economics, College of Administration and Economics, Nawroz University, Kurdistan Region, IraqIn this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. An application algorithm was proposed to transform the positive original responses using the first model and the stationary responses using the second model to improve the nonparametric estimation of the functional time series. The Box-Cox model contributed to improving the results of the nonparametric estimation of the original data, but the results become somewhat confusing after attempting to make the transformed response variable stationary in the mean, while the functional time series predictions were more accurate using the transformed stationary datasets using the Yeo-Johnson model.http://dx.doi.org/10.1155/2021/6676400
spellingShingle Sameera Abdulsalam Othman
Haithem Taha Mohammed Ali
Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves
Advances in Mathematical Physics
title Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves
title_full Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves
title_fullStr Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves
title_full_unstemmed Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves
title_short Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves
title_sort improvement of the nonparametric estimation of functional stationary time series using yeo johnson transformation with application to temperature curves
url http://dx.doi.org/10.1155/2021/6676400
work_keys_str_mv AT sameeraabdulsalamothman improvementofthenonparametricestimationoffunctionalstationarytimeseriesusingyeojohnsontransformationwithapplicationtotemperaturecurves
AT haithemtahamohammedali improvementofthenonparametricestimationoffunctionalstationarytimeseriesusingyeojohnsontransformationwithapplicationtotemperaturecurves