Step 4. GM-GRA-DPC-PSOSVR combined forecasting. The forecasting results of individual models are as the inputs to PSOSVR for establishing nonlinear combination, and the whole procedure based on Step 1 to Step 4 is called GM-GRA-DPC-PSOSVR model. Step 5. Forecasting comparisons. In order to test the effectiveness of the forecasting models, this research introduces MAPE(Mean Absolute Percentage Error), MAE(Mean Absolute Error), MSE, and improvement rates to compares the forecasting ability of GM-GRA-DPC-PSOSVR with individual forecasting models.

这段话也没有发现任何语法错误。该段介绍了GM-GRA-DPC-PSOSVR模型的构建过程中的第四步和第五步，即使用PSOSVR建立个体模型的非线性组合，整个过程基于步骤1到步骤4被称为GM-GRA-DPC-PSOSVR模型。为了测试预测模型的有效性，该研究引入MAPE（平均绝对百分比误差）、MAE（平均绝对误差）、MSE和改进率来比较GM-GRA-DPC-PSOSVR与个体预测模型的预测能力。

相关问题

Therefore, this research introduced the combined forecasting to overcome the model selection uncertainty, and proposed GM-GRA-DPC-PSOSVR nonlinear combination for carbon emission forecasting. The combined forecasting binds the information of individual models and meets the requirements of high adaptability and forecasting precision. Furthermore, the combined model GM-GRA-DPC-PSOSVR is suitable for small samples forecasting. The processes of GM-GRA-DPC-PSOSVR are as the following steps:

这段话也没有发现任何语法错误。该段介绍了该研究引入组合预测方法来克服模型选择的不确定性，并提出了GM-GRA-DPC-PSOSVR非线性组合模型用于碳排放预测。组合预测将个体模型的信息绑定在一起，并满足高适应性和预测精度的要求。此外，组合模型GM-GRA-DPC-PSOSVR适用于小样本预测。GM-GRA-DPC-PSOSVR的过程如下所示：

GM-GRA-DPC-PSOSVR forecasting model For any regional carbon emission forecasting, it is important to select a suitable model. For model selection, researchers often choose only one optimal model according to experience or model selection criteria, which obviously ignores the uncertainty of model selection. That is, once the data changes slightly, the optimal model may change, which means the optimal model is uncertain. The combined forecasting was proposed by Bates and Granger(1969), which is a robust forecasting technique.

这段话也没有发现任何语法错误。该段介绍了GM-GRA-DPC-PSOSVR预测模型的构建。对于任何地区的碳排放预测，选择一个合适的模型是非常重要的。在模型选择方面，研究人员通常根据经验或模型选择标准选择一个最优模型，这显然忽略了模型选择的不确定性。也就是说，一旦数据发生细微变化，最优模型可能会发生变化，这意味着最优模型是不确定的。Bates和Granger(1969)提出了组合预测方法，这是一种强大的预测技术。