Forecasting commodity prices in Brazil through hybrid SSA-complex seasonality models

Paper aims: To predict monthly corn, soybean, and sugar spot prices in Brazil using hybrid forecasting techniques. Originality: This study combines the Singular Spectrum Analysis with different forecasting methods. Research method: This paper presents a set of hybrid forecasting approaches combining Singular Spectrum Analysis (SSA) with different univariate time series methods, ranging from complex seasonality methods to machine learning and autoregressive models to predict monthly corn, soybean, and sugar spot prices in Brazil. We carry out a range of out-of-sample forecasting experiments and use a comprehensive set of forecast evaluation metrics. We contrast the performance of the proposed approaches with that of a range of benchmark models. Main findings: The results show that the proposed hybrid models present better performances, with the hybrid SSA-neural network approach providing the most competitive results in our sample. Implications for theory and practice: Forecasting agricultural prices is of paramount importance to assist producers, farmers, and the industry in decision-making processes.