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Accurate forecasting is important for decision-makers. Recently, the Amazonrainforest is reaching record levels of the number of fires, a situation thatconcerns both climate and public health problems. Obtaining the desiredforecasting accuracy becomes difficult and challenging. In this paper weredeveloped a novel heterogeneous decomposition-ensemble model by using Seasonaland Trend decomposition based on Loess in combination with algorithms forshort-term load forecasting multi-month-ahead, to explore temporal patterns ofAmazon rainforest fires in Brazil. The results demonstrate the proposeddecomposition-ensemble models can provide more accurate forecasting evaluatedby performance measures. Diebold-Mariano statistical test showed the proposedmodels are better than other compared models, but it is statistically equal toone of them.

Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model