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Forecasting the Area Flowing of Graduate Employment based on KRNN method

机译：基于KRNN方法预测研究生就业区域流动

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摘要

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.
机译：由于时间序列和复杂因子输入，难以搜索影响变量并预测研究生就业流动。最近，神经网络方法已成功用于解决问题。然而，由于非线性和噪音，预测结果并不理想。在这项工作中，通过将经常性神经网络（RNN）与内核主成分分析（KPCA）组合，提出了一种KRNN模型，基于该KRNN模型，试图预测研究生就业的区域，以及复杂因素问题和复杂因素问题时间序列问题已被处理。在模型中，作为特征提取的具​​有内核主成分分析（KPCA）和主成分分析（PCA）的RNN在比较中被引入。然后通过与来自中国中学的实际数据的实证研究，表明该方法可以实现与NN方法相比的良好预测性能。 KPCA方法比PCA方法更好。

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