# Feature-Based Time-Series Analysis in R using the theft Package

arXiv: https://arxiv.org/abs/2208.06146 · HTML: https://mlsanity.com/paper/2208.06146/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [Highly comparative feature-based time-series classification](https://mlsanity.com/paper/1401.3531/) (2014)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Distributed and parallel time series feature extraction for industrial big data applications](https://mlsanity.com/paper/1610.07717/) (2016)
- [Feature-based time-series analysis](https://mlsanity.com/paper/1709.08055/) (2017)
- [tsflex: flexible time series processing & feature extraction](https://mlsanity.com/paper/2111.12429/) (2021)
- [An Empirical Evaluation of Time-Series Feature Sets](https://mlsanity.com/paper/2110.10914/) (2021)
- [Highly comparative fetal heart rate analysis](https://mlsanity.com/paper/1412.1138/) (2014)

## Built on by (most cited first, 4 of 4)

- [Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification](https://mlsanity.com/paper/2303.17809/) (2023)
- [A Survey on Time-Series Distance Measures](https://mlsanity.com/paper/2412.20574/) (2024)
- [Estimating Time Series Foundation Model Transferability via In-Context Learning](https://mlsanity.com/paper/2509.23695/) (2025)
- [FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series](https://mlsanity.com/paper/2605.17282/) (2026)

## Abstract (arXiv)

Time series are measured and analyzed across the sciences. One method of quantifying the structure of time series is by calculating a set of summary statistics or `features', and then representing a time series in terms of its properties as a feature vector. The resulting feature space is interpretable and informative, and enables conventional statistical learning approaches, including clustering, regression, and classification, to be applied to time-series datasets. Many open-source software packages for computing sets of time-series features exist across multiple programming languages, including catch22 (22 features: Matlab, R, Python, Julia), feasts (42 features: R), tsfeatures (63 features: R), Kats (40 features: Python), tsfresh (779 features: Python), and TSFEL (390 features: Python). However, there are several issues: (i) a singular access point to these packages is not currently available; (ii) to access all feature sets, users must be fluent in multiple languages; and (iii) these feature-extraction packages lack extensive accompanying methodological pipelines for performing feature-based time-series analysis, such as applications to time-series classification. Here we introduce a solution to these issues in an R software package called theft: Tools for Handling Extraction of Features from Time series. theft is a unified and extendable framework for computing features from the six open-source time-series feature sets listed above. It also includes a suite of functions for processing and interpreting the performance of extracted features, including extensive data-visualization templates, low-dimensional projections, and time-series classification operations. With an increasing volume and complexity of time-series datasets in the sciences and industry, theft provides a standardized framework for comprehensively quantifying and interpreting informative structure in time series.