论文标题
基于合奏分解模型的亚马逊雨林大火的短期预测
Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model
论文作者
论文摘要
准确的预测对于决策者很重要。最近，亚马逊雨林正在达到火灾数量的创纪录水平，这种情况涉及气候和公共卫生问题。获得所需的预测准确性变得困难和挑战。在本文中，通过使用季节性和趋势分解，基于黄土与算法结合使用算法，用于短期负载预测多个月的趋势，以探索巴西亚马逊雨林火灾的时间模式。结果表明，提出的分解汇总模型可以提供通过绩效指标评估的更准确的预测。 Diebold-Mariano统计测试表明，所提出的模型比其他比较模型更好，但在统计学上等于其中的模型。
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.
