【统计学】【2001.05】离散时间序列中的模式定位

本文为美国南佛罗里达大学（作者：KEVIN B. PRATT）的硕士论文，共83页。

我们描述了一种快速压缩时间序列的技术，对得到的压缩序列进行索引，并检索与给定模式相似的序列。压缩算法识别时间序列中的“重要”点并丢弃其他点。该算法在线性时间内运行，需要恒定的内存，并且对各种各样的时间序列都有很好的结果。我们不仅将重要点用于压缩，还用于索引时间序列数据库，支持对模式的有效搜索，并允许用户在搜索速度和准确性之间进行权衡。实验证明了所开发的技术在股票价格、气象数据和心电图识别模式的有效性。

We describe a technique for fast compression of time-series, indexing of the resulting compressed series, and retrieval of series similar to a given pattern. The compression algorithm identifies “important” points of a time-series and discards the other points. It runs in linear time, takes constant memory, and gives good results for a wide variety of time-series. We use the important points not only for compression, but also for indexing a database of time-series, which supports efficient search for patterns and allows the user to control the trade-off between the speed and accuracy of search. The experiments show the effectiveness of the developed technique for identifying patterns in stock prices, meteorological data, and electrocardiograms.

1. 引言
2. 前期工作
3. 重要点
4. 相似性测量
5. 模式检索
6. 结论

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