NONLINEAR FORECASTING OF DAILY TRAFFIC FLOW BASED ON OPTIMAL EMBEDDING PHASE-SPACE

摘要：

Traffic flow prediction is an important application in ITS. This paper presents a new method to build a nonlinear forecasting model for daily traffic flow prediction. The method consists of three steps. First, a statistic is offered to determine whether a linear model or a nonlinear model is suitable for a given time series. Second, if a nonlinear model is suitable, then a new algorithm is approved to synchronously select the optimal embedding dimension and delay step of the time series constructed phase-space. Last, a local linear forecasting model based on the optimal embedding phase-space is build. The real daily traffic flow data are applied to test the new method.

关键词： Time series Phase-space embedding Traffic flow forecasting Nonlinear

作者: HONG XIE ZHONG-HUA LIU HONG-QIONG HUANG

作者单位: College of Information Engineering, Shanghai Maritime University, Shanghai, 200135, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1341-1346

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）