Application of Regularization and Cross-Validation in Combination Forecasting Model
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    Abstract:

    To determine the weight of the combined forecasting model is very important to improve the accuracy of the model. Applying the regularization and cross-validation to the combined forecasting model based on the least squares method is for studying whether the regularization and cross-validation can improve the prediction effect of the combined forecasting model. It is carried out by adding the L1 and L2 norm regularization terms to the optimization solution of the combined model and using leave-one-out-cross-validation in the data set. The result shows that both the L1 and L2 norm regularization can improve prediction accuracy of the combined model to a certain degree. Moreover, the L1 norm regularization is better than the L2 norm regularization for the combined forecasting model, and the more single forecasting models participating in the combined forecasting, the better the regularization improvement effect. In addition, there is a positive correlation between the cross-validation improvement effect and amount of experimental data given.

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张欣怡,袁宏俊.正则化和交叉验证在组合预测模型中的应用.计算机系统应用,2020,29(4):18-23

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History
  • Received:June 16,2019
  • Revised:July 12,2019
  • Online: April 09,2020
  • Published: April 15,2020
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