Basic theory analysis of support vector regression in time series forecasting is introduced in detail and a multi-step forecasting formula is presented, Final Prediction Error(FPE) ( FPE ) principle is suggested to select the embedding dimension. 详细分析了支持向量机用于时间序列预测的理论基础,并给出了运用支持向量回归进行多步预测的一般公式,提出了用最终预报误差(FPE)准则优化选取嵌入维数。
This text adopts Final Prediction Error(FPE) ( FPE ) criterion and Akaike's Information Criterion ( AIC ) to confirm autoregressive model order. 文章中采用了应用比较广泛的最终预测误差(FPE)(FPE)准则和阿凯克信息论准则(AIC),最终确定AR模型的阶数。
Otherwise, to save the resource Akaike's final prediction error standard ( FPE ) is employed to delete the nodes that contribute little to the outputs of the network. This will balance the precision with the complexity of the network. 另外,从节省资源的角度出发,本文采用了Akaike的最终预报误差标准FPE,删除那些对网络输出贡献较小的节点以取得网络精度与复杂度的平衡。