A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting

作者：

Highlights：

• The new scheme determines prediction uncertainty to consider the value of forecasting.

• The signal decomposition technique reveals the stochastic characteristics of sequences.

• The single-model weight of the ensemble model is determined along with its ability.

摘要

•The new scheme determines prediction uncertainty to consider the value of forecasting.•The signal decomposition technique reveals the stochastic characteristics of sequences.•The single-model weight of the ensemble model is determined along with its ability.

论文关键词：Precipitation,Probabilistic forecasting,Ensemble model,Signal decomposition techniques,Long-short-term memory network,Adaptive Metropolis Markov Chain Monte Carlo

论文评审过程：Received 9 August 2020, Revised 5 July 2021, Accepted 3 September 2021, Available online 11 September 2021, Version of Record 16 September 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115872