相似性度量是金融时间序列挖掘中的一项关键技术，但现有的度量方法不适合分析小规模的金融多元时间序列。
Similarity measure is a key technology of time series mining, whose existing methods are not available for the analysis of small-scale multivariate time series.
提出了计算几何应用到时间序列挖掘的方法，实现了时间序列全序列匹配查询、模式查询、反向查询和异常检测，查询效率和准确性都有了比较大的提高。
By making use of the proximity query method in computational geometry, the whole matching query, pattern query, inverse query and outlier detection in time series are studied.
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。
Rough set theory, as an effective tool to deal with vagueness and uncertainty, is effective to the time series data mining.
应用推荐