{"id":21844234,"url":"https://github.com/pastas/metran","last_synced_at":"2025-08-17T16:37:06.345Z","repository":{"id":42627867,"uuid":"345959954","full_name":"pastas/metran","owner":"pastas","description":"Multivariate timeseries analysis using dynamic factor modelling.","archived":false,"fork":false,"pushed_at":"2024-02-20T15:23:13.000Z","size":15572,"stargazers_count":21,"open_issues_count":3,"forks_count":6,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-04-09T18:51:47.267Z","etag":null,"topics":["analysis","groundwater","hydrology","multivariate","pastas","python","timeseries"],"latest_commit_sha":null,"homepage":"https://metran.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pastas.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":"2021-03-09T09:57:48.000Z","updated_at":"2024-11-13T10:09:58.000Z","dependencies_parsed_at":"2024-06-19T02:46:30.185Z","dependency_job_id":"338c7eb8-6360-47f7-b701-c8251020a160","html_url":"https://github.com/pastas/metran","commit_stats":{"total_commits":109,"total_committers":4,"mean_commits":27.25,"dds":"0.27522935779816515","last_synced_commit":"e30c0bb3ac9ce6b161ecf8aac77f4cd45f89c731"},"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pastas%2Fmetran","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pastas%2Fmetran/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pastas%2Fmetran/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pastas%2Fmetran/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pastas","download_url":"https://codeload.github.com/pastas/metran/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248877958,"owners_count":21176244,"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":["analysis","groundwater","hydrology","multivariate","pastas","python","timeseries"],"created_at":"2024-11-27T22:18:47.882Z","updated_at":"2025-04-14T12:11:23.111Z","avatar_url":"https://github.com/pastas.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![metran](https://github.com/pastas/metran/actions/workflows/ci.yml/badge.svg)](https://github.com/pastas/metran/actions/workflows/ci.yml)\n[![Documentation Status](https://readthedocs.org/projects/metran/badge/?version=latest)](https://metran.readthedocs.io/en/latest/?badge=latest)\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/43056ec3f22341fa992fff4e7b2eeb73)](https://www.codacy.com/gh/pastas/metran/dashboard?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=pastas/metran\u0026utm_campaign=Badge_Grade)\n[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/43056ec3f22341fa992fff4e7b2eeb73)](https://www.codacy.com/gh/pastas/metran/dashboard?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=pastas/metran\u0026utm_campaign=Badge_Coverage)\n![PyPI](https://img.shields.io/pypi/v/metran)\n\n# Metran\n\nMetran is a package for performing multivariate timeseries analysis using a\ntechnique called dynamic factor modelling. It can be used to describe the\nvariation among many variables in terms of a few underlying but unobserved\nvariables called factors.\n\n## Documentation\n\nThe documention can be found on [metran.readthedocs.io](https://metran.readthedocs.io/)\n\n### Examples\n\nFor a brief introduction of the theory behind Metran on multivariate timeseries analysis with\ndynamic factor modeling see the notebook:\n\n- [The Dynamic Factor Model](https://github.com/pastas/metran/blob/main/examples/dynamic_factor_model.ipynb)\n\nA practical real-world example, as published in Stromingen (Van Geer, 2015), is given in the following notebook:\n\n- [Metran practical example](https://github.com/pastas/metran/blob/main/examples/metran_practical_example.ipynb)\n\nA notebook on how to use [Pastas](https://github.com/pastas/pastas) models output with Metran:\n\n- [Pastas Metran example](https://github.com/pastas/metran/blob/main/examples/pastas_metran_example.ipynb)\n\n## Installation\n\nTo install Metran, a working version of Python 3.8 or higher has to be installed on your computer.\nWe recommend using the [Anaconda distribution](https://www.anaconda.com/) as it includes most\nof the python package dependencies and the Jupyter Notebook software to run the\nnotebooks. However, you are free to install any Python distribution you want.\n\nTo install `metran`, type the following command\n\n`pip install metran`\n\nTo install in development mode, clone the repository and type the following from the module root directory:\n\n`pip install -e .`\n\n### Dependencies\n\nMetran has the following dependencies which are automatically installed if\nnot already available: `numpy`, `scipy`, `pandas`, `matplotlib`, `numba` and `pastas`\n\n## References\n\n- Berendrecht, W.L. (2004). [State space modeling of groundwater fluctuations](https://repository.tudelft.nl/islandora/object/uuid:f12775fc-a804-4d4a-8872-664e5a61cbf5/datastream/OBJ). Doctoral Thesis, Delft University of Technology.\n- Berendrecht, W.L., F.C. van Geer (2016). [A dynamic factor modeling framework for analyzing multiple groundwater head series simultaneously](http://dx.doi.org/10.1016/j.jhydrol.2016.02.028). Journal of Hydrology, 536, pp. 50-60.\n- Van Geer, F.C. en W.L. Berendrecht (2015) [Meervoudige tijdreeksmodellen en de samenhang in stijghoogtereeksen](https://edepot.wur.nl/378871). Stromingen 23 nummer 3, pp. 25-36.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpastas%2Fmetran","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpastas%2Fmetran","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpastas%2Fmetran/lists"}