A novel sub-models selection algorithm based on max-relevance and min-redundancy neighborhood mutual information

Xiao, Litian; Wang, Chen; Dong, Yunxuan; Wang, Jianzhou

doi:10.1016/j.ins.2019.01.075

Cited by 15 publications

(1 citation statement)

References 29 publications

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“…When the probability distribution of variables and their joint distribution are known, mutual information can measure the correlation between different random variables (Zhang et al 2020a ). However, probability distributions are difficult to measure in practice (Xiao et al 2019 ). Therefore, this paper introduces the neighborhood mutual information (NMI) (Hu et al 2011 ) measure to the correlation between two variables, without calculating the probability distribution of dataset.…”

Section: Introductionmentioning

confidence: 99%

Multi-step prediction of carbon emissions based on a secondary decomposition framework coupled with stacking ensemble strategy

Zhang

Ling

Zeng

et al. 2023

Environ Sci Pollut Res

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Accurate prediction of carbon emissions is vital to achieving carbon neutrality, which is one of the major goals of the global effort to protect the ecological environment. However, due to the high complexity and volatility of carbon emission time series, it is hard to forecast carbon emissions effectively. This research offers a novel decomposition-ensemble framework for multi-step prediction of short-term carbon emissions. The proposed framework involves three main steps: (i) data decomposition. A secondary decomposition method, which is a combination of empirical wavelet transform (EWT) and variational modal decomposition (VMD), is used to process the original data. (ii) Prediction and selection: ten models are used to forecast the processed data. Then, neighborhood mutual information (NMI) is used to select suitable sub-models from candidate models. (iii) Stacking ensemble: the stacking ensemble learning method is innovatively introduced to integrate the selected sub-models and output the final prediction results. For illustration and verification, the carbon emissions of three representative EU countries are used as our sample data. The empirical results show that the proposed framework is superior to other benchmark models in predictions 1, 15, and 30 steps ahead, with the mean absolute percentage error (MAPE) of the proposed framework being as low as 5.4475% in Italy dataset, 7.3159% in France dataset, and 8.6821% in Germany dataset.

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Section: Introductionmentioning

confidence: 99%