Abstract
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.
Highlights
Forecasting the future is the main function of time series analysis
The two authors believe that this problem may be exacerbated in some time series data, especially those that are characterized by the presence of seasonal changes
It is important to note that the optimum power parameters λ∗ for both transformation models are significantly different even though Yeo-Johnson transformations (YJT) represents the extended version of the Box-Cox transformation (BCT) model
Summary
Forecasting the future is the main function of time series analysis. Proceeding from this idea, researchers have developed several techniques that are concerned with the improvement of accuracy of forecasts by treating the time series as a stochastic process. The two authors believe that this problem may be exacerbated in some time series data, especially those that are characterized by the presence of seasonal changes It has become known in practical applications of time series that they are rarely stationary and that seasonal changes, trend, and dependence on external factors have become the rules, not the exception [5]. The two authors have used the Yeo-Johnson transformations to improve the nonparametric estimation of the functional time series The use of both approaches, transformation, and functional analysis without considering the modeling conditions is an attempt to focus the analyzing goal and the efficiency criterion in the context of forecast ability. The practical examples are included in the fourth section, while the fifth section contained some conclusions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
