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

Autor:	da Silva, Ramon Gomes, Ribeiro, Matheus Henrique Dal Molin, Mariani, Viviana Cocco, Coelho, Leandro dos Santos
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages with 3 figures; Comments edited
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.07979 Zobrazit plný text záznamu View this record from Arxiv