According to the character of non linear network traffic, the traffic time series is decomposed into trend component, period component, mutation component and random component.
文章考虑网络流量非线性的特点，通过不同的数学模型将流量时间序列分解成趋势成分、周期成分、突变成分和随机成分。
Taking noise recognition and noise reduction of traffic volume time series which are commonly used traffic data as example, several experimental results are illustrated.
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。
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.
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。
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.
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。
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.
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 results show that the self similarity of the time series varies greatly when the parameters are changed, that is, the parameters have a great effect on the self similar traffic.
研究结果表明，当混沌映射的系统参数变化时，所产生的时间序列的自相似特性也发生变化，即系统参数对混沌映射产生自相似业务流具有很大的影响。
Then the mechanism of the chaotic learning algorithm is described, and the adaptive learning algorithm of WNN for traffic flow time series is designed.
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。
The ARIMA model has been applied to evaluate and predict the time series of macroscopic traffic volume.
应用ARIMA模型，对宏观交通量时间序列进行模型估计和预测。
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.
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。
The performances of some classic time series prediction models were analyzed together concerning the traffic prediction of General Packets Radio Service (GPRS) cells.
针对通用无线分组业务(GPRS)小区流量预测问题，对几种典型时序预测模型的性能进行了综合分析。
A combined dynamic method of forecast traffic volume time series is proposed.
提出了一种预测交通流量的动态组合建模方法。
A combined dynamic method of forecast traffic volume time series is proposed.
提出了一种预测交通流量的动态组合建模方法。
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