Being able to accurately forecast the evolution of wheat prices can be a valuable tool. Most of the published works apply classical forecasting models to wheat price time series, and they do not always perform out-of-sample testing. This work compares five modelling approaches for wheat price forecasts, using only past values of the time series. The models performance is assessed considering out-of-sample data only, by considering a sliding and growing time window that will define the data used to determine the models parameters, and the data used for out-of-sample forecasts.
CITATION STYLE
Dias, J., & Rocha, H. (2019). Forecasting Wheat Prices Based on Past Behavior: Comparison of Different Modelling Approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11621 LNCS, pp. 167–182). Springer Verlag. https://doi.org/10.1007/978-3-030-24302-9_13
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