  Accurate forecasting is important for decision-makers. Recently, the Amazon
rainforest is reaching record levels of the number of fires, a situation that
concerns both climate and public health problems. Obtaining the desired
forecasting accuracy becomes difficult and challenging. In this paper were
developed a novel heterogeneous decomposition-ensemble model by using Seasonal
and Trend decomposition based on Loess in combination with algorithms for
short-term load forecasting multi-month-ahead, to explore temporal patterns of
Amazon rainforest fires in Brazil. The results demonstrate the proposed
decomposition-ensemble models can provide more accurate forecasting evaluated
by performance measures. Diebold-Mariano statistical test showed the proposed
models are better than other compared models, but it is statistically equal to
one of them.
