Short-term traffic volume time series forecasting based on phase space reconstruction

被引：0

作者：

Chen, SY ^{[1
]}

Zhou, YH ^{[1
]}

Wang, W ^{[1
]}

机构：

[1] Nanjing Normal Univ, Optoelect Key Lab Jiangsu Prov, Nanjing 210097, Peoples R China

来源：

Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 | 2005年

关键词：

short-term traffic volume; forecasting; nearest neighbor equal distance method; phase space reconstruction;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A method for the short-term prediction of traffic volume time series owning chaos characteristics based on reconstruction of phase space is described. The principle of nearest neighbor equal distance method in phase space is introduced, and this approach is firstly applied to forecast a real traffic volume time series and obtain the forecasting result of the traffic volume, and the forecasting result is compared with the results obtained by neural network and gray mode with one rank & one variable (abbreviated as GM (1,1)). The experiments prove that the short-term traffic volume forecasting based on phase space reconstruction is valid and feasible.

引用

页码：3585 / 3588

页数：4

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