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

TL;DR

This paper introduces a comprehensive framework for organizing and analyzing large collections of real-world and model-generated time series and their analysis methods, enabling automated method selection and cross-disciplinary insights.

Contribution

It presents a novel approach to systematically organize time-series data and analysis algorithms using reduced representations, facilitating cross-disciplinary comparisons and automated method selection.

Findings

Organized over 35,000 time series and 9,000 analysis algorithms.

Demonstrated utility on EEG, heart rate, speech, and other datasets.

Developed tools for automatic classification and regression of time series.

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 analyzing 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 analyzed 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, heart beat 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.