The prediction of carbon price can contribute to the reduction of carbon emissions. A carbon price forecasting model based on the data decomposition method, adaptive boosting (AdaBoost) algorithm, and Elman neural network (ENN) is proposed in this paper, which firstly decomposes original data into subsequences by the variational mode decomposition algorithm, and then combines the ENN models by the AdaBoost.RT algorithm to forecast each subsequence, and finally the predicted results of each subsequence are combined into the final prediction results. Using the carbon price in Beijing, China as the experimental data, the evaluation errors certify that the proposed ensemble model can achieve a better forecasting effect than the single models including the ENN, extreme learning machine, and long short-term memory network and the ensemble model AdaBoost.RT-ENN, which proves the effectiveness of the data decomposition and adaptive boosting algorithm.
The prediction of traffic flow can supply a powerful reference for urban traffic planning. A multi-step traffic flow prediction model using stacking ensemble learning algorithm is constructed in this paper, which ensembles three Elman Neural Networks (ENN) as base learn and a Support Vector Machine (SVM) as meta-learner. Experimental results prove that the good multi-step prediction effect can be achieved by the proposed stacking ensemble model, and compared with Autoregressive Integrated Moving Average (ARIMA), ENN, SVM and Echo State Networks (ESN), the proposed model can achieve the highest prediction accuracy regardless of 1, 2 or 3 step forecasting.
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