Abstract
This article introduces a novel algorithm for incorporating power transformation into the estimation process of a Holt-Winters Seasonal model. The algorithm outlines a series of steps aimed at selecting the most appropriate power parameter estimate. This selection is achieved using the conventional Maximum Likelihood Estimation method in combination with various criteria for enhancing statistical modeling efficiency. Supplementary decision rules include assessing Mean Square Error, Mean Absolute Error, and conducting a p-value test for the normality of errors. The algorithm's effectiveness is demonstrated through its application to real-world data. Ultimately, the article affirms the feasibility of obtaining viable solutions for selecting the optimal power parameter.
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.
