In the past decade, deep learning models have shown to be promising tools for time series forecasting. However, owing to significant differences in the volatility characteristics among different types of time series data, it is difficult for an individual deep learning model to maintain robust forecasting performance. In this study, a novel ensemble deep learning model is proposed to achieve accurate and stable time series forecasting. First, a boosting deep learning method based on extended AdaBoost algorithm is proposed for generating various basic predictors. These basic predictors are further enhanced through a new dynamic error correction method. A stacking-based ensemble method that employs kernel ridge regression as the meta-predictor is then used to combine the basic predictors to produce the ultimate forecasting results. To increase forecasting accuracy and stability, an enhanced multi-population non-dominated sorting genetic algorithm-II is proposed for ensemble pruning. Finally, the forecasting performance of the proposed model is verified through the use of three different types of real-world time series data (i.e., PM_2.5 concentration, wind speed, and electricity price). The experimental results showed that the proposed model is superior to other baseline models in dealing with time series forecasting tasks.

在过去的十年中，深度学习模型已证明是用于时间序列预测的有前途的工具。但是，由于不同类型的时间序列数据之间的波动性特征存在显着差异，因此单个深度学习模型难以维持稳健的预测性能。在这项研究中，提出了一种新颖的集成深度学习模型，以实现准确，稳定的时间序列预测。首先，提出了一种基于扩展AdaBoost算法的Boosting深度学习方法，用于生成各种基本预测变量。这些基本的预测变量通过一种新的动态错误校正方法得到了进一步增强。然后使用基于核岭回归作为元预测变量的基于堆栈的集成方法来组合基本预测变量以产生最终的预测结果。为了提高预测的准确性和稳定性，提出了一种改进的多种群非支配排序遗传算法-II。最后，通过使用三种不同类型的现实世界时间序列数据（即PM）来验证所提出模型的预测性能_2.5浓度，风速和电价）。实验结果表明，该模型在处理时间序列预测任务方面优于其他基线模型。