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This Author published in this journals
All Journal Journal of Applied Science, Engineering, Technology, and Education JINAV: Journal of Information and Visualization Daengku: Journal of Humanities and Social Sciences Innovation ARRUS Journal of Mathematics and Applied Science
Miguel Botto-Tobar
(1) Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (2) Research Group in Artificial Intelligence and Information Technology, University of Guayaquil, 090510, Guayaquil, Ecuador
Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Other

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Journal : Daengku: Journal of Humanities and Social Sciences Innovation

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The Comparison of Single and Double Exponential Smoothing Models in Predicting Passenger Car Registrations in Canada Ansari Saleh Ahmar; Sitti Masyitah Meliyana; Miguel Botto-Tobar; Rahmat Hidayat
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 4 No. 2 (2024)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku2639

Abstract

This study aims to compare the two main variants of exponential smoothing methods in the context of business forecasting: Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES). In this study, we applied these three methods to the data on Monthly Passenger Car Registrations in Canada from 2019 to 2022. The performance of each method was evaluated using Root Mean Square Error (RMSE) as the primary metric. The analysis results showed that Single Exponential Smoothing (SES) produced the best performance with the lowest RMSE of 13.07859 for an alpha of 0.6, compared to DES, which yielded higher RMSE values. These findings indicate that although DES have the capability to handle trends and seasonality, in some cases, especially when the data has single fluctuations without significant seasonal patterns or trends, SES can provide more accurate forecasting results. This study provides valuable insights for practitioners in selecting the most appropriate forecasting method based on the characteristics of the data at hand.
Co-Authors Ahmar, Ansari Saleh Angela Diaz Cadena R. Rusli Rahman, Abdul Rahmat Hidayat Ruliana Sitti Masyitah Meliyana R. Taufiq Hidayat Zulkifli Rais