This article proposes a hybrid model based on regressor combination to improve the accuracy of air‐quality forecasting. The expectation‐maximization algorithm was used to impute the missing values of the dataset. The optimal hyperparameter values for the regressors were found by the grid search approach, depending on the mean absolute error (MAE), in the training session. The regressors having the minimum MAE were then globally combined for prediction. The output of the regressor with the minimum absolute error between the actual and predicted values was chosen as the prediction result of the hybrid model. The performance of the proposed model was compared with that of sequential deep learning methods, namely long short‐term memory and gated recurrent unit, in terms of MAE, mean relative error (MRE), and squared correlation coefficient (SCC) metrics. The imputed dataset was divided into training and testing subsets of different durations. According to the experimental results, our hybrid model performed better than the deep learning methods in terms of MAE, MRE, and SCC metrics, irrespective of the training data length. Furthermore, the Akaike's information criterion and the Bayesian information criterion values suggested that the quality of the hybrid model was better than that of the deep learning models.

中文翻译：

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使用回归器结合缺失的数据插补来提高空气质量预报的准确性

本文提出了一种基于回归组合的混合模型，以提高空气质量预报的准确性。期望最大化算法用于估算数据集的缺失值。通过网格搜索方法，在训练过程中根据平均绝对误差（MAE）找到了回归变量的最佳超参数值。然后将具有最小MAE的回归变量进行全局组合以进行预测。选择实际值和预测值之间的绝对误差最小的回归器的输出作为混合模型的预测结果。将所提出模型的性能与顺序深度学习方法（即长短期记忆和门控递归单元）的MAE，平均相对误差（MRE），和平方相关系数（SCC）指标。估算的数据集分为不同持续时间的训练和测试子集。根据实验结果，无论训练数据的长度如何，我们的混合模型在MAE，MRE和SCC指标方面的表现均优于深度学习方法。此外，Akaike信息准则和贝叶斯信息准则值表明，混合模型的质量优于深度学习模型。