Ensemble methods for advanced skier days prediction

•We propose the use of ensemble learning techniques to improve skier days forecasts.•We utilize actual data containing skier days across six ski seasons, from a regional ski resort.•Three base prediction models as inputs to four ensemble modeling techniques are analyzed.•Stacking shows the highest increase in accuracy across five independent variable versions.•An operational prediction model is offered. The tourism industry has long utilized statistical and time series analysis, as well as machine learning techniques to forecast leisure activity demand. However, there has been limited research and application of ensemble methods with respect to leisure demand prediction. The research presented in this paper appears to be the first to compare the predictive power of ensemble models developed from multiple linear regression (MLR), classification and regression trees (CART) and artificial neural networks (ANN), utilizing local, regional, and national data to model skier days. This research also concentrates on skier days prediction at a micro as opposed to a macro level where most of the tourism applications of machine learning techniques have occurred. While the ANN model accuracy improvements over the MLR and CART models were expected, the significant accuracy improvements attained by the ensemble models are notable. This research extends and generalizes previous ensemble methods research by developing new models for skier days prediction using data from a ski resort in the state of Utah, United States.