Time-Series Similarity Queries Employing a Feature-Based Approach
Time-Series Similarity Queries Employing a Feature-Based Approach
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发表时间：
1999
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通讯作者：
R. Alcock
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作者：
R. Alcock
1.汇总时间序列或时间序列数据显示一段时间内参数的值。对时间序列数据的一种常见查询是找到与给定序列相似的所有序列。评估两个序列之间相似性的最常见技术是计算它们之间的欧几里得距离。然而，可以给出许多例子，其中两个相似的序列被大的欧几里德距离分开。本文不直接计算序列之间的欧几里德距离，而是将序列转化为特征向量，然后计算特征向量之间的欧几里德距离。实验结果表明，该方法在寻找相似序列方面具有较大的优势。
1. SUMMARY Time-series, or time-sequence, data show the value of a parameter over time. A common query with time-series data is to find all sequences which are similar to a given sequence. The most common technique for evaluating similarity between two sequences involves calculating the Euclidean distance between them. However, many examples can be given where two similar sequences are separated by a large Euclidean distance. In this paper, instead of calculating the Euclidean distance directly between two sequences, the sequences are transformed into a feature vector and the Euclidean distance between the feature vectors is then calculated. Results show that this approach is superior for finding similar sequences.