Forecasting passenger car registrations in Canada: A hybrid exponential smoothing ensemble with inverse-RMSE weighting

Ahmar, Ansari Saleh, Al Idrus, Salim, Val, Eva Boj del and Rizal, Muh. (2026) Forecasting passenger car registrations in Canada: A hybrid exponential smoothing ensemble with inverse-RMSE weighting. International Journal on Advanced Science, Engineering and Information Technology (IJASEIT), 16 (3). pp. 982-989. ISSN 2088-5334 ; E-ISSN 2460-6952

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Abstract

Accurate forecasting of automotive market demand is critical for infrastructure planning, emissions policy, and smart city investment. Despite widespread use of exponential smoothing methods in transportation research, no published study has systematically evaluated a hybrid SES–DES ensemble with empirically calibrated inverse-RMSE weighting for automotive registration forecasting during an extreme market disruption. This study develops and validates a hybrid framework using 39 monthly observations of passenger car registrations in Canada (January 2019–March 2022), sourced from the OECD database. The framework integrates Single Exponential Smoothing (SES) and Double Exponential Smoothing (DES) through an inverse-RMSE weighting mechanism that allocates weights of 0.519 (SES) and 0.481 (DES), with parameter optimization performed via an exhaustive grid search over α ∈ [0.01, 0.99] with a step size of 0.01. Model performance is assessed using RMSE, MAE, MAPE, and sMAPE across training, validation, and test sets, supplemented by rolling-window validation and residual diagnostics. The hybrid model achieves a test-set RMSE of 12.42, representing improvements of 5.0% over standalone SES (RMSE = 13.08) and 11.8% over standalone DES (RMSE = 14.08). MAE and MAPE exhibit comparable improvement patterns. Robustness tests confirm stable performance across rolling windows and across the COVID-19-induced structural break (Chow F = 18.34, p < 0.001). These findings demonstrate that simple ensemble strategies applied to classical time-series methods yield meaningful gains in accuracy without sacrificing interpretability. The framework offers immediate practical value for transportation planners and urban policymakers seeking reliable, computationally efficient forecasting tools for volatile market environments.

Item Type:	Journal Article
Keywords:	hybrid forecasting; exponential smoothing; weighted average; automotive industry; ensemble methods
Subjects:	13 EDUCATION > 1303 Specialist Studies In Education > 130302 Comparative and Cross-Cultural Education 13 EDUCATION > 1399 Other Education > 139999 Education not elsewhere classified
Divisions:	Faculty of Economics > Department of Management
Depositing User:	Prof. Dr. Salim Al Idrus
Date Deposited:	24 Sep 2026 13:55

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