时间序列数据离散化

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摘要翻译：
数据离散化，也称为binning，是计算机科学、统计学及其在生物数据分析中的应用中经常使用的技术。提出了一种将实值数据离散化为有限个离散值的新方法。该方法的新方面是将信息论准则和确定最优值数的准则结合在一起。虽然该方法可以用于数据聚类，但其发展的动机是需要一种离散化算法来处理多个多元时间序列的异构数据，如转录本、蛋白质和代谢物浓度测量。由于生化网络的几种建模方法都采用离散变量状态，因此需要保持变量之间的相关性以及时间序列的动态特性。该算法的C++实现可从http://polymath.vbi.vt.edu/discreatization上获得。
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英文标题：
《Discretization of Time Series Data》
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作者：
Elena S. Dimitrova, John J. McGee, Reinhard C. Laubenbacher
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最新提交年份：
2005
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Data discretization, also known as binning, is a frequently used technique in computer science, statistics, and their applications to biological data analysis. We present a new method for the discretization of real-valued data into a finite number of discrete values. Novel aspects of the method are the incorporation of an information-theoretic criterion and a criterion to determine the optimal number of values. While the method can be used for data clustering, the motivation for its development is the need for a discretization algorithm for several multivariate time series of heterogeneous data, such as transcript, protein, and metabolite concentration measurements. As several modeling methods for biochemical networks employ discrete variable states, the method needs to preserve correlations between variables as well as the dynamic features of the time series. A C++ implementation of the algorithm is available from the authors at http://polymath.vbi.vt.edu/discretization .
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PDF链接：
https://arxiv.org/pdf/q-bio/0505028

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关键词：时间序列数据 序列数据 时间序列 离散化 Quantitative 建模 Data series 测量 vbi