堆积
集合预报
人工神经网络
集成学习
系列(地层学)
计算机科学
人工智能
分解
机器学习
模式识别(心理学)
数据挖掘
化学
古生物学
有机化学
生物
作者
Piotr S. Maciąg,Robert Bembenik,Aleksandra Piekarzewicz,Javier Del Ser,Jesús L. Lobo,Nikola Kasabov
标识
DOI:10.1016/j.envsoft.2023.105851
摘要
In this article, we introduce a new approach to air pollution prediction using the CEEMDAN time series decomposition method combined with the two-layered ensemble of predictors created based on the stacking and bagging techniques. The proposed ensemble approach is outperforming other selected state-of-the-art models when the bagging ensemble consisting of evolving Spiking Neural Networks (eSNNs) is used in the second layer of the stacking ensemble. In our experiments, we used the PM10 air pollution and weather dataset for Warsaw. As the results of the experiments show, the proposed ensemble can achieve the following error and agreement values over the tested dataset: error RMSE 6.91, MAE 5.14 and MAPE 21%; agreement IA 0.94. In addition, this article provides the computational and space complexity analysis of eSNNs predictors and offers a new encoding method for spiking neural networks that can be effectively applied for values of skewed distributions.
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