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

arXiv: https://arxiv.org/abs/1304.1209 · HTML: https://mlsanity.com/paper/1304.1209/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2013.

## Built on by (most cited first, 30 of 30)

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- [An Empirical Evaluation of Time-Series Feature Sets](https://mlsanity.com/paper/2110.10914/) (2021)
- [Feature-Based Time-Series Analysis in R using the theft Package](https://mlsanity.com/paper/2208.06146/) (2022)
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- [Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data](https://mlsanity.com/paper/2204.06843/) (2022)
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- [Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series](https://mlsanity.com/paper/2212.06146/) (2022)
- [Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification](https://mlsanity.com/paper/2509.22919/) (2025)
- [Visualising Deep Network's Time-Series Representations](https://mlsanity.com/paper/2103.07176/) (2021)
- [Clustering of longitudinal data: A tutorial on a variety of approaches](https://mlsanity.com/paper/2111.05469/) (2021)
- [Hydroclimatic time series features at multiple time scales](https://mlsanity.com/paper/2112.01447/) (2021)
- [Time Series Data Mining Algorithms Towards Scalable and Real-Time Behavior Monitoring](https://mlsanity.com/paper/2112.14630/) (2021)
- [Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions](https://mlsanity.com/paper/2303.16117/) (2023)
- [Forecasting large collections of time series: feature-based methods](https://mlsanity.com/paper/2309.13807/) (2023)
- [Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation](https://mlsanity.com/paper/2312.01344/) (2023)
- [Understanding Mechanistic Role of Structural and Functional Connectivity in Tau Propagation Through Multi-Layer Modeling](https://mlsanity.com/paper/2510.20148/) (2025)
- [Data Complexity-aware Deep Model Performance Forecasting](https://mlsanity.com/paper/2601.01383/) (2026)
- [Do Models Hear Like Us? Probing the Representational Alignment of Audio LLMs and Naturalistic EEG](https://mlsanity.com/paper/2601.16540/) (2026)
- [Modular Foundation Models for Time-Series Perception in Digital Twins](https://mlsanity.com/paper/2607.03585/) (2026)
- [Statistical comparisons of time-series feature sets on classification tasks](https://mlsanity.com/paper/2608.01586/) (2026)

## Abstract (arXiv)

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.