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.

本文通过使用季节性和趋势性分解基于 Loess 的多月前短期负荷预测算法及异构分解集成模型，探讨巴西亚马逊雨林火灾的时间模式，结果表明所提出的模型可以提供更准确的预测，并通过性能指标进行评估，Diebold-Mariano 统计检验显示所提出的模型优于其他对比模型，但与其中一个模型在统计上相等。