Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
粗糙集理论作为一种处理模糊和不确定性问题的有效工具，对时间序列的数据挖掘是有效的。
Since urban travel times are stochastic and uncertain, a model for addressing urban travel time prediction by using transport information granular computing theory based on rough set was proposed.
由于城市道路交通问题具有不确定性和不精确性，故采用基于粗糙集的交通信息提取计算理论建立城市道路行程时间预测模型。
That using the reduction attributes of rough set reduced some redundant attributes, improved the real time of data processing by support vector machine, and shorten the time for training sample.
利用粗糙集的属性约简性来约简掉一些冗余属性，提高了支持向量机进行数据处理的实时性，缩短了训练样本的时间。
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