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

By Ben D. Fulcher, Max A. Little, Nick S. Jones. Journal of the Royal Society Interface, 2013

403 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ee4fba9dbfd571d15d3ea23c3f274be829648825

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Citations in the corpus

catch22: CAnonical Time-series CHaracteristics (2019, methodology): “This ‘highly-comparative’ approach involves comparison across a comprehensive collection of thousands of diverse time-series features and has recently been operationalized as the hctsa (highly comparative time-series analysis) toolbox (Fulcher et al. 2013; Fulcher and Jones 2017, 2014).”
Highly Comparative Feature-Based Time-Series Classification (2014, methodology): “Using an extensive database of algorithms for measuring thousands of diﬀerent time-series properties (developed in previous work [4]), here we show that general feature-based representations of time series can be used to tackle classiﬁcation problems in time-series data mining.”
Forecasting: theory and practice (2020, background): “(Nanopoulos et al., 2001; Fulcher et al., 2013), anomaly detection (Kang, 2012; Talagala et al.”
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting (2018, methodology): “Our large-scale experiment was performed on 45 datasets compiled from different sources [46, 47, 48].”
A practical guide to linking brain-wide gene expression and neuroimaging data (2018, methodology): “A variety of outlier-robust normalizations exist such as Hampel hyperbolic tangent transformation, however here we focus on a variant of a normalization method used by Fulcher and Fornito (2016), the scaled robust sigmoid (SRS) normalization (Fulcher et al., 2013).”