改进SVR及其在铁路客运量预测中的应用

Improved Support Vector Regression and Its Application to Prediction of Railway Passenger Traffic Volume

摘要: 为了提高铁路客运量现有预测方法的预测能力,用训练样本与测试样本间的马氏距离对惩罚因子进行加权,对传统的支持向量回归机(SVR)进行了改进,在此基础上提出了基于改进SVR的铁路客运量时间序列预测方法.以1980~1998年铁路客运量预测为例,对SVR方法和BP人工神经网络(BPANN)方法进行了比较,结果表明,SVR方法能获得更准确的预测结果.
- 铁路客运量 /
- 支持向量回归机 /
- 人工神经网络 /
- 时间序列预测
Abstract: To improve the prediction abilities of the present methods for railway passenger traffic volume,support vector regression(SVR)was improved by weighting penalty coefficients using Mahalanobis distance between training and testing samples,and a model for predicting the time serial of railway passenger traffic volume was set up based on the improved SVR.The prediction of railway passenger traffic volume from 1980 to 1998 shows that the proposed method can obtain a more accurate result than the BP artificial neural network.
- railway passenger traffic volume /
- support vector regression /
- artificial neural network /
- time serial prediction

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