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A New Dynamic Indexing Structure for Searching Time-Series Patterns

机译:一种用于搜索时间序列模式的新动态索引结构

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摘要

We target at the growing topic of representing and searching time-series data. A new MABI (Moving Average Based Indexing) technique is proposed to improve the performance of the similarity searching in large time-series databases. Notions of Moving average and Euclidean distances are introduced to represent the time-series data and to measure the distance between two series. Based on the distance reducing rate relation theorem, the MABI technique has the ability to prune the unqualified sequences out quickly in similarity searches and to restrict the search to a much smaller range, compare to the data in question. Finally the paper reports some results of the experiment on a stock price data set, and shows the good performance of MABI method.
机译:我们针对表示和搜索时间序列数据的日益增长的主题。为了提高大型时间序列数据库中相似性搜索的性能,提出了一种新的基于移动平均的索引技术。引入了移动平均和欧几里得距离的概念来表示时间序列数据并测量两个序列之间的距离。基于距离减少率关系定理,与相关数据相比,MABI技术具有在相似性搜索中快速修剪掉不合格序列的能力,并将搜索范围限制在很小的范围内。最后,本文在股票价格数据集上报告了一些实验结果,并证明了MABI方法的良好性能。

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