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The process of collecting and organizing sets of observations represents acommon theme throughout the history of science. However, despite the ubiquityof scientists measuring, recording, and analyzing the dynamics of differentprocesses, an extensive organization of scientific time-series data andanalysis methods has never been performed. Addressing this, annotatedcollections of over 35 000 real-world and model-generated time series and over9000 time-series analysis algorithms are analyzed in this work. We introducereduced representations of both time series, in terms of their propertiesmeasured by diverse scientific methods, and of time-series analysis methods, interms of their behaviour on empirical time series, and use them to organizethese interdisciplinary resources. This new approach to comparing acrossdiverse scientific data and methods allows us to organize time-series datasetsautomatically according to their properties, retrieve alternatives toparticular analysis methods developed in other scientific disciplines, andautomate the selection of useful methods for time-series classification andregression tasks. The broad scientific utility of these tools is demonstratedon datasets of electroencephalograms, self-affine time series, heart beatintervals, speech signals, and others, in each case contributing novel analysistechniques to the existing literature. Highly comparative techniques thatcompare across an interdisciplinary literature can thus be used to guide morefocused research in time-series analysis for applications across the scientificdisciplines.

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