Research on error correction method based on data transformational GM (1, 1)

It is of great significance to improve the prediction effect of forecasting method, however experience shows that it is very difficult for improving the accuracy of forecasting by setting up single forecasting model. This article describes the deficiencies of the present forecasting methods and puts forward a new approach for the improvement of prediction accuracy by introducing error correction. First, the fuzzy neural network forecasting model is established for a preliminary prediction by using the training sample data. Second, the data transformation is introduced to process the error sequence. On the basis of the processed data, the GM (1, 1) model is constructed and is used to predict the subsequent error. Third, the correction of preliminary prediction values is calibrated. The numerical example based on the historical data of the Shanghai composite index shows that the presented approach improves the accuracy of forecasting significantly compared with the prediction accuracy before correction, and then the validity of the model is verified.