To accurately capture the characteristics of the time series data, we extracted four feature sets based on the lag length, moving average, variational mode decomposition, and empirical mode decomposition methods. This study then proposes a two-stage heterogeneous ensemble method for predicting carbon trading prices. All parties involved in carbon trading aim to obtain the maximum benefit from it, and this requires participants to accurately judge the carbon trading price.
Several countries have formulated carbon-neutral plans in dealing with global warming, which have also derived various carbon trading markets. Moreover, the importance of each feature was evaluated, which has important implications for future work such as casualty prevention and rescue during earthquakes. It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy. Experiments were conducted to compare the proposed method with popular machine learning methods. To verify the effectiveness of the model, we collected data pertaining to earthquake destruction from 1966 to 2017 in China.
To obtain accurate prediction results, an effective prediction method based on stacking ensemble learning and improved swarm intelligence algorithm is proposed in this study, which comprises three parts: (1) applying multiple base learners for training, (2) using a stacking strategy to integrate the results generated by multiple base learners to obtain the final prediction results, and (3) developing an improved swarm intelligence algorithm to optimize the key parameters in the prediction model. The number of casualties is determined by various factors, necessitating a comprehensive system for earthquake-casualty prediction. The estimation of the loss and prediction of the casualties in earthquake-stricken areas are vital for making rapid and accurate decisions during rescue efforts.
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