pyhctsa: A Python package for highly-comparative time-series analysis

[joshuabmoore]

Submitting Author: (@joshuabmoore)
All current maintainers: (@joshuabmoore, @benfulcher)
Package Name: pyhctsa
One-Line Description of Package: Python package for highly comparative time-series analysis
Repository Link: https://github.com/DynamicsAndNeuralSystems/pyhctsa
Version submitted: 0.1.2
EiC: @crhea93
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

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As time-series data grows increasingly massive and complex, researchers rely on extracting "features", statistical summaries of data patterns, to distill meaningful patterns and insights from these datasets.pyhctsa is a native Python library designed to bridge the gap between advanced time-series analysis and modern open-source workflows. It ports the massive library of the original and highly successful MATLAB-based "Highly Comparative Time-Series Analysis" (HCTSA) framework into Python, enabling users to automatically extract and compare thousands of diverse time-series features from their data.

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  • Data retrieval
  • Data extraction
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  • Who is the target audience and what are scientific applications of this package?

The target audience of pyhctsa encompasses a broad spectrum of the global research community, ranging from data scientists and physicists to biologists and engineers. The impact of pyhctsa is expected to be as far-reaching as its predecessor, the original MATLAB HCTSA framework, for which scientific utility has been proven through a myriad of publications across diverse disciplines including neuroscience, medicine, geoscience, engineering, and others. By bringing the framework to the Python open-source ecosystem, the package will facilitate seamless integration with modern machine learning workflows and pipelines.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

tsfresh - Limited set of time-series features, most of which comprise simple distributional, spectral and a few hand-selected information-theoretic measures.
tsfel - Limited in scope, with time-series feature set restricted to only a handful of basic distributional, spectral and only a handful of nonlinear analysis-based features.
Kats - Limited scope as far as breadth of time-series features. Package seems to not be maintained in the past 3-4 years. Many open issues which have not been resolved.

Compared to the above packages that rely on a narrow, a priori selection of descriptive statistics, pyhctsa provides a truly comprehensive, interdisciplinary library of over 4000 features, ranging from nonlinear dynamics and scaling laws, to information theory and model-based quantities. By casting a wider net across time-series analysis methods, pyhctsa shifts the focus from simple feature extraction to scientific discovery, enabling the identification of the dynamical "fingerprints" that underlie time-series data across diverse problems. As the Python successor to the MATLAB-based HCTSA, long considered the gold standard in highly comparative time-series analysis, pychtsa intends to uphold the same level of depth and rigour.

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[crhea93]

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Editor comments

Thank you for this submission. I've broken my comments into two main categories below:

Major

• While the tutorial notebooks are very useful, this package is missing a well structured and detailed documentation. I suggest that the authors use a tool like sphinx. Please note that we require API documentation as well. The exact requirements can be found earlier in this comment.

• PyOpenSci requires a robust AI disclosure section in the README file. Although this is a constantly changing policy as we deal with an ever-changing agentic LLM landscape, we have crafted this policy outline https://www.pyopensci.org/blog/generative-ai-peer-review-policy.html

Minor

• The automated tests are very useful. However, it would be very helpful to have a coverage report so that we can ensure enough code is being covered by the tests.

I’ve observed that the majority of the codebase was initially developed in September, with a number of more recent, sporadic contributions over the past month. This pattern may be worth noting in the project documentation or release notes, as it could provide useful context for contributors or users regarding the project’s development timeline and current focus areas.
Question for Clarification:
Could you share any background on why the development activity was concentrated in September, followed by a period of less frequent updates? Understanding the project’s development rhythm can help ensure that the documentation and community expectations are aligned with the actual workflow.

[crhea93]

I've added some comments to the original post @joshuabmoore

[joshuabmoore]

Hi @crhea93,
Thank you for taking the time to perform the pre-review checks.
Regarding the revisions:

Major:

• Sphinx documentation (with API) for pyhctsa is now available at https://dynamicsandneuralsystems.github.io/pyhctsa/
• An AI disclosure has been added to the README here

Minor:

• A coverage report has been added. Coverage is now summarised with the badge on the README.

Development Activity
The concentration of commits in September reflects a repository migration and consolidation phase rather than the start of the project's development. The core functionality and codebase was originally developed and iterated on over a much longer period (July 2024 - July 2025) in a separate repository available here. In September, the codebase was reorganised, refactored, and migrated into the current public repository to formalise the package structure, documentation, testing, and release pipeline.

[crhea93]

@joshuabmoore thank you very much for addressing this! I will begin looking for an editor :)

[joshuabmoore]

@crhea93
Appreciate it! Thank you :)