本文在分别对基于平均速度和基于平均通过时间的算法误差分析的基础上，提出了基于浮动车技术的城市路况计算方法。
Based on the error analysis of the algorithms based on average speed and average travel time, this paper promoted the urban traffic situation evaluation methods based on probe vehicle data.
通过时间序列分析建立反映切削状态的数学模型，从动态数据中凝聚信息，构成用于判别的特征向量。
By time series analysis, we build models depicting the cutting tool states, coacervate information from dynamic date and construct feature vectors for discrimination.
为了克服历史数据不足的问题，设计了通过时间序列聚类分析进行学习样本集的积累的方法。
To overcome the shortage of historical data, the increment of learning samples are got by clustering analysis the time series data from Ticket sale record.
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