{"id":13528604,"url":"https://github.com/sktime/pysf","last_synced_at":"2026-04-02T02:53:55.247Z","repository":{"id":62583409,"uuid":"139419682","full_name":"sktime/pysf","owner":"sktime","description":"Supervised forecasting of sequential data in Python.","archived":false,"fork":false,"pushed_at":"2019-01-02T15:36:28.000Z","size":5038,"stargazers_count":55,"open_issues_count":2,"forks_count":8,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-02-10T09:18:45.503Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sktime.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}},"created_at":"2018-07-02T09:16:25.000Z","updated_at":"2023-05-25T18:01:07.000Z","dependencies_parsed_at":"2022-11-03T22:16:29.653Z","dependency_job_id":null,"html_url":"https://github.com/sktime/pysf","commit_stats":null,"previous_names":["ahmedgc/pysf","alan-turing-institute/pysf"],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fpysf","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fpysf/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fpysf/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fpysf/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sktime","download_url":"https://codeload.github.com/sktime/pysf/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246655174,"owners_count":20812591,"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":[],"created_at":"2024-08-01T07:00:21.716Z","updated_at":"2026-04-02T02:53:55.223Z","avatar_url":"https://github.com/sktime.png","language":"Python","readme":"\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://badge.fury.io/py/pysf\"\u003e\u003cimg src=\"https://badge.fury.io/py/pysf.svg\" alt=\"pysf version\" height=\"18\"\u003e\u003c/a\u003e\n  \u003ca href=\"https://opensource.org/licenses/BSD-3-Clause\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-BSD%203--Clause-blue.svg\" alt=\"License\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n![pysf](https://github.com/alan-turing-institute/pysf/raw/master/docs/_static/logo.png)\n\nSupervised forecasting of sequential data in Python.\n\n## Intro\n\n_Supervised forecasting_ is the machine learning task of making predictions for sequential data like time series (_forecasting_) by exploiting independent examples of the same underlying relationship (_supervised learning_). Learning is flexible enough to incorporate metadata as well as sequential data.\n\n## Package features\n\n* Store and safely manipulate multi-series data and its metadata in a custom data container.\n* Define your own machine learning prediction strategies to operate on this data. Make use of tuning and pipelining objects to build composite prediction strategies. Use the widely-adopted fit/predict workflow throughout.\n* Plug in classical forecasting or supervised learning-based predictors into a framework that adapts them to the supervised forecasting task. Interface with popular machine learning \u0026 forecasting frameworks, such as [scikit-learn](https://scikit-learn.org/stable/), [keras](https://keras.io/) and [statsmodels](https://www.statsmodels.org/stable/index.html). \n* Empirically estimate multiple predictors' generalisation performance using nested resampling schemes, in a statistically sound manner. Compare predictors to baselines.\n\n\n## Getting started\n\n### Documentation\n\n* Have a look at the [demonstration Jupyter notebook](examples/Walkthrough.ipynb) for a tutorial.\n* [API documentation](https://alan-turing-institute.github.io/pysf) is hosted on GitHub Pages.\n\n### Installation\n\nYou can install pysf using the [pip](https://pypi.org/project/pysf/) package management system. If you have pip installed, simply run\n```\npip install pysf\n```\nto install the latest release of pysf.\n\nIn addition to the package, you will need the following prerequisites to take advantage of pysf's full functionality.\n\n### Prerequisites:\n\n* [pandas](https://pandas.pydata.org/pandas-docs/stable/install.html) 0.20 or higher\n* [keras](https://keras.io/#installation) 2.0 or higher\n* [scikit-learn](https://scikit-learn.org/stable/install.html)\n* [xarray](http://xarray.pydata.org/en/stable/installing.html)\n* [scipy](https://scipy.org/install.html)\n* [numpy](https://scipy.org/install.html)\n* [matplotlib](https://matplotlib.org/users/installing.html)\n\nThese are also required, but should be part of your Python distribution:\n* abc\n* logging\n\nTo use keras for deep learning:\n* Make sure you [install](https://keras.io/#installation) keras and at least one backend engine. pysf has been tested against TensorFlow and Theano as backends. \n* If using TensorFlow as a backend, you will typically need to install [dask](http://docs.dask.org/en/latest/install.html) 0.15 or higher.\n\n## Contributions\n\n### How to cite\n\nComing soon!\n\n### How to contribute\n\nWe welcome contributions! \n\n* You can suggest new features or report bugs by creating a [new issue](https://github.com/alan-turing-institute/pysf/issues/new). Please check the [list of issues](https://github.com/alan-turing-institute/pysf/issues) first.\n* If you have made a change for an open issue, please submit a pull request linking to that issue.\n* If you would like to improve the documentation, please go right ahead and submit a pull request.\n\n### Contributors\n\n* Ahmed Guecioueur ([@ahmedgc](https://github.com/ahmedgc)) is the original author of this package.\n\n### Copyright and license\n\nCode and documentation copyright 2018 [Ahmed Guecioueur](https://github.com/ahmedgc). Code released under the BSD-3-Clause License. ","funding_links":[],"categories":["Examples or singular models"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fpysf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsktime%2Fpysf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fpysf/lists"}