The wisdom of crowds in forecasting at high-frequency for multiple time horizons: A case study of the Brazilian retail sales

Abstract

This case study compares the forecasting accuracy obtained for four daily Brazilian retail sales indexes at four time prediction horizons. The performance of traditional time series forecasting models, artificial neural network architectures and machine learning algorithms were compared in other to evaluate the existence of a single best performing model. Afterwards, ensemble methods were added to model comparison to verify if accuracy improvement could be obtained. Evidence found in this case study suggests that a consistent forecasting strategy exists for the Brazilian retail indexes by applying both seasonality treatment for holidays and calendar effects and by using an ensemble method which main inputs are the predictions of all models with calendar variables. This strategy was consistent across all 16 index and time horizon combinations since ensemble methods either outperformed the best single models or there were no statistical difference from them in a Diebold-Mariano's test.