A KPCA RNN Based Model for the Area Flowing of Graduate Employment Forecasting

Searching influence variables as well as forecasting the flowing of graduate employment is an ongoing activity of considerable significance. But the forecasting is complex due to the time series and complex factor inputs. The neural network method has been successfully employed to solve the multi factors problem. However the forecasting result is not ideal due to the nonlinearity and noise. In this work, a neural network model is presented by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA). And then try to forecast the area flowing of graduate employment using this model. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data from some high school of China, it is shown that the proposed methods can both achieve good forecasting performance comparing with NN method. And the KPCA method performs better than the PCA method.