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.
Bibliographical noteFunding Information:
J. L. Lobo and J. Del Ser receive funding support from the Basque Government, Spain through grants KK-2023/00012 (BEREZ-IA) and IT1456-22 (MATHMODE) .
© 2023 The Author(s)
- Air pollution prediction
- Evolving spiking neural networks
- Bagging ensembles
- Stacking ensembles
- Time series decomposition