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中文摘要：
确定时间序列的相似性匹配方法都没有考虑数据的不确定性，而现实世界中诸如温度传感器等设备采集到的数据往往是不确定的，并且两条不确定时间序列之间的距离也是不确定的，所以现有的确定时间序列的相似性匹配方法不适用于这些领域。针对此问题，提出了基于统计学的规约算法，并且基于该算法提出了不确定时间序列相似性匹配的两种新型算法。在规约过程中，规约算法优化了不同背景下不确定时间序列的小概率点和奇异点的处理。在匹配过程中，首先提出了圆环匹配算法，它通过构建匹配圆环完成相似性匹配，并且通过多次重启提高相似性匹配的准确度和效率；然后在规约算法的基础上，提出了期望匹配的改进算法，它通过增加包络约束消除期望匹配算法中出现的误判问题。
英文摘要：
Similarity matching techniques for certain time series did not consider the uncertainty of the data,but the data col-lected by the sensors were often not certain in the real world.So,the existed similarity matching methods of time series did not apply to these areas.To solve this problem,this paper put forward a reduction algorithm based on statistics and improved the Euclidean distance calculation.Then,it raised new similarity matching algorithms.In the process of reduction,it optimized the treatment of small probability points and singular points in every slot.In the process of circle similarity matching,it im-proved the accuracy and decreased the time cost by restarting many times.At last,it put forward the improved algorithm by ex-pectancy calculation based on reduction algorithm and solved the miscarriage of justice problem.
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