CAJ | 학술논문

讨论了人工神经网络在金融汇率预报中的应用。其中介绍了广义交互验证(Generalized Cross Validation)法如何应用于确定神经网络中隐层的个数，并用实例说明了该方法甚至对复杂的非线性函数也可以得到很好的逼近。详细地介绍了运用人工神经网络作两周向前汇率预报的计算步骤。其平均相对误差(APE)为10*E-3的数量级，而国际上通用的状态空间模型及Box-Jenkins的ARIMA模型的预报误差都在10*E-2的数量级。
토론료인공신경망락재금융회솔예보중적응용。기중개소료엄의교호험증(Generalized Cross Validation)법여하응용우학정신경망락중은층적개수，병용실례설명료해방법심지대복잡적비선성함수야가이득도흔호적핍근。상세지개소료운용인공신경망락작량주향전회솔예보적계산보취。기평균상대오차(APE)위10*E-3적수량급，이국제상통용적상태공간모형급Box-Jenkins적ARIMA모형적예보오차도재10*E-2적수량급。
The main purpose of this paper is to investigate the application of the neuron network (NN) for the daily exchange rate forecasting.Generalized Cross Validation (GCV) is introduced to determine the number of nodes of the hidden layer,several well known time series forecasting methods are also compared with the NN method in this paper.