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.
阐述了混沌学习算法的机理，设计了交通流量WNN混沌时间序列自适应学习算法。
Then the mechanism of the chaotic learning algorithm is described, and the adaptive learning algorithm of WNN for traffic flow time series is designed.
本文阐述了离散时间点过程理论，时变马尔科夫链及鞅差分序列在城市交通车队状态观测器中的应用。
This paper presents some applications of discrete - time point process theory, time - varying Markov chain and Martingle difference in urban traffic computer-based control systems.
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.
为了解决日益严重的城市交通问题，本文根据交通流已被证明的混沌特性，尝试采用非线性混沌模型来分析交通流时间序列。
In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non linear chaotic model to analyze the time series of traffic flow is proposed.
基于短时交通量时间序列的随机波动特征，提出一种小波分析和模糊马尔柯夫结合的预测方法。
Based on the dynamic and stochastic characteristic of short-term traffic volume, an approach combined wavelet analysis and fuzzy Markov forecasting model is put forward.
应用ARIMA模型，对宏观交通量时间序列进行模型估计和预测。
The ARIMA model has been applied to evaluate and predict the time series of macroscopic traffic volume.
本文采用改进型BP神经网络建立起交通流的时间序列模型，该模型可用于短期内道路交通流量的预测。
In this paper, the time - sequence model of traffic flow is based on the improved BP neural network, and this model can be used for short time prediction of traffic flow.
通过实验研究，发现基于灰色关联度的层次化聚类方法能较好地实现交通流时间序列的进一步有效分离。
The experiments show that the proposed method can work and the gray relation grade measure is better suited for the problem than the dynamic time warping measure.
采用聚类分析方法对交通流时间序列进行分析可以发现典型的交通流变化模式。
By clustering of traffic flow time series, the typical traffic fluctuation patterns can be found.
并将该方法应用于15分钟和5分钟采样周期的实测交通流时间序列中，计算结果表明交通序列中含有混沌特性。
Then, we use this method into the real time series of traffic flow with sampling period of 15 and 5 minutes, the results indicate that there are chaos in the traffic flow.
结果表明，该模型能够较准确地预测交通流量时间序列和低维混沌时间序列。
Experimental results show that the proposed Volterra adaptive prediction model is capable of effectively predicting traffic flow time sequence and low-dimensional chaotic time sequence.
把最终的季节时间序列模型转化为状态空间形式，通过卡尔曼滤波实时调整状态向量，实现电梯交通流的在线预测。
It transforms the finial SARIMA model to state space model, adjusts the state vector using Kalman filter, and realizes the on-line forecast.
把最终的季节时间序列模型转化为状态空间形式，通过卡尔曼滤波实时调整状态向量，实现电梯交通流的在线预测。
It transforms the finial SARIMA model to state space model, adjusts the state vector using Kalman filter, and realizes the on-line forecast.
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