On the Use of the Power Transformation Models to Improve the Temperature Time Series

Statistics, Optimization and Information Computing

article

Sameera AbdulsalamOthman¹  Haithem Taha Mohammed Ali² 

[1] University of Dohuk;University of Zakho

关键词: Box-Cox transformation;    Yeo-Johnson transformation;    ARIMA models.;   

DOI  :  10.19139/soic-2310-5070-1333

来源: Istituto Superiore di Sanita

【 摘 要 】

The aim of this paper is to select an appropriate ARIMA model for the time series after transforming the original responses. Box-Cox and Yeo-Johnson power transformation models were used on the response variables of two time series datasets of average temperatures and then diagnosed and built the appropriate ARIMA models for each time-series. The authors treat the results of the model fitting as a package in an attempt to decide and choose the best model by diagnosing the effect of the data transformation on the response normality, significant of estimated model parameters, forecastability and the behavior of the residuals. The authors conclude that the Yeo-Johnson model was more flexible in smoothing the data and contributedto accessing a simple model with good forecastability.

【 授权许可】

Unknown