Highly comparative time-series analysis: the empirical structure of time series and their methods.
Highly comparative time-series analysis: the empirical structure of time series and their methods.
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高度比较的时间序列分析：时间序列的经验结构及其方法。
DOI：
10.1098/rsif.2013.0048
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发表时间：
2013-06-06
期刊：
影响因子：
--
通讯作者：
Jones NS
中科院分区：
文献类型：
--
作者：
Fulcher BD;Little MA;Jones NS
收集和组织观察集的过程代表了整个科学史上的一个共同主题。然而，尽管测量、记录和分析不同进程动态的科学家无处不在，但从未对科学时间序列数据和分析方法进行过广泛的组织。针对这一点，超过35 000个真实世界和模型生成的时间序列，并超过9000个时间序列分析算法的注释集合进行了分析，在这项工作中。我们介绍了减少表示的时间序列，在他们的属性测量不同的科学方法，和时间序列分析方法，在他们的行为方面的经验时间序列，并用它们来组织这些跨学科的资源。这种比较不同科学数据和方法的新方法使我们能够根据其属性自动组织时间序列数据集，检索其他科学学科中开发的特定分析方法的替代方案，并自动选择用于时间序列分类和回归任务的有用方法。这些工具的广泛的科学效用被证明对脑电图，自仿射时间序列，心跳间隔，语音信号和其他数据集，在每种情况下，贡献新的分析技术，现有的文献。因此，跨学科文献的高度比较技术可以用于指导跨学科应用的时间序列分析中更有重点的研究。
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.
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