{"id":13571025,"url":"https://github.com/DynamicsAndNeuralSystems/catch22","last_synced_at":"2025-04-04T07:32:46.479Z","repository":{"id":37939358,"uuid":"146194807","full_name":"DynamicsAndNeuralSystems/catch22","owner":"DynamicsAndNeuralSystems","description":"catch22: CAnonical Time-series CHaracteristics","archived":false,"fork":false,"pushed_at":"2024-12-25T23:06:49.000Z","size":56680,"stargazers_count":377,"open_issues_count":12,"forks_count":69,"subscribers_count":10,"default_branch":"main","last_synced_at":"2024-12-26T22:20:49.046Z","etag":null,"topics":["feature-extraction","time-series","time-series-analysis"],"latest_commit_sha":null,"homepage":"https://time-series-features.gitbook.io/catch22","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DynamicsAndNeuralSystems.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-08-26T15:57:14.000Z","updated_at":"2024-12-26T14:00:36.000Z","dependencies_parsed_at":"2023-10-14T23:30:13.029Z","dependency_job_id":"7e2af270-67d6-41c0-89ae-c98a972c6f9e","html_url":"https://github.com/DynamicsAndNeuralSystems/catch22","commit_stats":null,"previous_names":["chlubba/catch22"],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fcatch22","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fcatch22/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fcatch22/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DynamicsAndNeuralSystems%2Fcatch22/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DynamicsAndNeuralSystems","download_url":"https://codeload.github.com/DynamicsAndNeuralSystems/catch22/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247024134,"owners_count":20870938,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["feature-extraction","time-series","time-series-analysis"],"created_at":"2024-08-01T14:00:57.636Z","updated_at":"2025-04-04T07:32:41.470Z","avatar_url":"https://github.com/DynamicsAndNeuralSystems.png","language":"C","funding_links":[],"categories":["📦 Packages"],"sub_categories":["Python"],"readme":"\u003cp align=\"center\"\u003e\u003cimg src=\"img/catch22_logo_square.png\" alt=\"catch22 logo\" height=\"220\"/\u003e\u003c/p\u003e\n\n\u003ch1 align=\"center\"\u003e\u003cem\u003ecatch22\u003c/em\u003e: CAnonical Time-series CHaracteristics\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n \t\u003ca href=\"https://zenodo.org/badge/latestdoi/146194807\"\u003e\u003cimg src=\"https://zenodo.org/badge/146194807.svg\" height=\"20\"/\u003e\u003c/a\u003e\n    \u003ca href=\"https://www.gnu.org/licenses/gpl-3.0\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" height=\"20\"/\u003e\u003c/a\u003e\n \t\u003ca href=\"https://twitter.com/compTimeSeries\"\u003e\u003cimg src=\"https://img.shields.io/twitter/url/https/twitter.com/compTimeSeries.svg?style=social\u0026label=Follow%20%40compTimeSeries\" height=\"20\"/\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n_catch22_ is a collection of 22 time-series features coded in C that can be run from Python, R, Matlab, and Julia, licensed under the [GNU GPL v3 license](http://www.gnu.org/licenses/gpl-3.0.html) (or later).\nThe _catch22_ features are a high-performing subset of the over 7000 features in [_hctsa_](https://github.com/benfulcher/hctsa).\n\nThe features were selected based on their classification performance across a collection of 93 real-world time-series classification problems, as described in our open-access paper, [\u0026#x1F4D7; Lubba et al. (2019). _catch22_: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x).\n\n## [\u0026#x1F4D9;\u0026#x1F4D8;\u0026#x1F4D7;___catch22_ documentation__](https://time-series-features.gitbook.io/catch22/)\n\nThere is [comprehensive documentation](https://time-series-features.gitbook.io/catch22/) for _catch22_, including:\n\n- Installation instructions (across C, python, R, Julia, and Matlab)\n- Information about the theory behind and behavior of each of the features,\n- A list of publications that have used or extended _catch22_\n- And more :yum:\n\n## Installation and Usage in Python, R, Matlab, Julia, and compiled C\n\nThere are also native versions of this code for other programming languages:\n\n- [Rcatch22](https://github.com/hendersontrent/Rcatch22) (R) `install.packages(\"Rcatch22\")`\n- [pycatch22](https://github.com/DynamicsAndNeuralSystems/pycatch22) (python) `pip install pycatch22`\n- [Catch22.jl](https://github.com/brendanjohnharris/Catch22.jl) (Julia) `Pkg.add(\"Catch22\")`\n\nYou can also use the C-compiled features directly, or in Matlab, following the [detailed installation instructions on the wiki](https://time-series-features.gitbook.io/catch22/matlab#installation).\n\n## Acknowledgement :+1:\n\nIf you use this software, please read and cite this open-access article:\n\n- \u0026#x1F4D7; Lubba et al. [_catch22_: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x), _Data Min Knowl Disc_ __33__, 1821 (2019).\n\n## Performance Summary\n\nSummary of the performance of the _catch22_ feature set across 93 classification problems, and a comparison to the [_hctsa_ feature set](https://github.com/benfulcher/hctsa) (cf. Fig. 4 from [our paper](https://doi.org/10.1007/s10618-019-00647-x)):\n\n![](img/PerformanceComparisonFig4.png)\n\n## Notes\n\n- When presenting results using _catch22_, you must identify the version used to allow clear reproduction of your results. For example, `CO_f1ecac` was altered from an integer-valued output to a linearly interpolated real-valued output from v0.3.\n- _catch22_ features only evaluate _dynamical_ properties of time series and do not respond to basic differences in the location (e.g., mean) or spread (e.g., variance).\n- If the location and spread of the raw time-series distribution may be important for your application, you should apply the function argument `catch24 = true` (`TRUE` in R, `True` in Python) to your call to the _catch22_ function in the language of your choice. This will result in 24 features being calculated: the _catch22_ features in addition to mean and standard deviation.\n- Time series are _z_-scored internally (for features other than mean and standard deviation), which means that, e.g., constant time series will lead to `NaN` outputs.\n- Time-series data are taken as an ordered sequence of values (without time stamps). We assume an evenly sampled time series.\n- See language-specific usage information in the [wiki](https://time-series-features.gitbook.io/catch22/matlab#getting-started-basic-usage).\n- The computational pipeline used to generate the _catch22_ feature set is in the [`op_importance`](https://github.com/chlubba/op_importance) repository.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDynamicsAndNeuralSystems%2Fcatch22","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDynamicsAndNeuralSystems%2Fcatch22","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDynamicsAndNeuralSystems%2Fcatch22/lists"}