利用实例仿真验证表明，LS-SVM具有较好的泛化能力和很强的鲁棒性，采用基于LS-SVM的交通流时间序列模型补齐丢失数据能够取得很好的效果。
The model for filling time series data of traffic flow based on LS-SVM is proposed in this paper, missing data can be filled by using traffic flow historical data.
针对交通流量混沌时间序列多步预测的问题，提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
以常用的交通数据———交通量时间序列的实测数据为例，给出多个噪声识别及消噪预处理的实验结果。
Taking noise recognition and noise reduction of traffic volume time series which are commonly used traffic data as example, several experimental results are illustrated.
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