AUTHORS: Sjoert Fleurke
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ABSTRACT: The aim of this paper is to check in practice to what extent an ensemble forecast based on averaging the outcomes of several forecasting methods provides better results than single forecasts. Therefore, we use data of monthly new car registrations in the Netherlands and car sales in the USA. The performances of seven popular forecasting methods are assessed and the results are combined into Ensemble forecasts. Several common performance metrics are applied on the results of the test data and it is shown that the Ensembles perform slightly better than each of the forecasting models separately. This confirms the idea, found in literature, that under certain conditions, a combination of several forecasts leads to more accurate results.
KEYWORDS: Automobile Registrations, Automobile Sales, ARIMA, Artificial Neural Network, Ensemble Forecasting, Exponential Smoothing, Generalized Linear Model, Theta, Time Series Forecasting, Random Forest, Vector Auto Regression
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