The Comparison of Single and Double Exponential Smoothing Models in Predicting Passenger Car Registrations in Canada

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.