Forecasting the Area Flowing of Graduate Employment Based on KRNN method

It is hard to search the influence variables and to forecast the flowing of graduate employment due to the time series and complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the forecasting result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the area flowing of graduate employment is tried to be forecasted, and both the complex factor problem and time series problem has been dealt with. 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.