Forecasting Commodity Prices in Brazil Through Hybrid SSA-Complex Seasonality Models

EasyChair Preprint 6652

Abstract

Forecasting agricultural prices is of paramount importance to assist producers, farmers, and the industry in the decision-making process. We propose a hybrid forecasting approach combining Singular Spectrum Analysis (SSA) with complex seasonality methods, machine learning, and auto-regressive models to predict monthly corn, soybean, and sugar spot prices in Brazil.

Keyphrases: Commodities, Forecasting, Singular Spectrum Analysi

@booklet{EasyChair:6652,
  author    = {Rafael Baptista Palazzi and Paula Maçaira and Erick Meira and Marcelo Klotzle},
  title     = {Forecasting Commodity Prices in Brazil Through Hybrid SSA-Complex Seasonality Models},
  howpublished = {EasyChair Preprint 6652},
  year      = {EasyChair, 2021}}