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Highly comparative time-series analysis: the empirical structure of time series and their methods

Title: Highly comparative time-series analysis: the empirical structure of time series and their methods
Authors: Fulcher, BD
Little, MA
Jones, NS
Item Type: Journal Article
Abstract: 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.
Issue Date: 3-Apr-2013
Date of Acceptance: 8-Mar-2013
URI: http://hdl.handle.net/10044/1/43173
DOI: http://dx.doi.org/10.1098/rsif.2013.0048
ISSN: 1742-5689
Publisher: Royal Society, The
Journal / Book Title: Journal of the Royal Society Interface
Volume: 10
Issue: 83
Copyright Statement: © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
time-series analysis
signal processing
longitudinal data analysis
time-series classification
time-series regression
Data Interpretation, Statistical
Heart Rate
Models, Theoretical
Research Design
Speech Articulation Tests
General Science & Technology
MD Multidisciplinary
Publication Status: Published
Article Number: ARTN 20130048
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences