{"id":19811540,"url":"https://github.com/adamvvu/tsfracdiff","last_synced_at":"2025-05-01T08:33:02.052Z","repository":{"id":57676925,"uuid":"489161219","full_name":"adamvvu/tsfracdiff","owner":"adamvvu","description":"Efficient and easy to use fractional differentiation transformations for stationarizing time series data in Python.","archived":false,"fork":false,"pushed_at":"2023-02-10T12:23:00.000Z","size":491,"stargazers_count":21,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-21T13:52:47.424Z","etag":null,"topics":["data-science","machine-learning","python","quantitative-finance"],"latest_commit_sha":null,"homepage":"","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/adamvvu.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":"2022-05-06T00:03:21.000Z","updated_at":"2025-03-19T21:59:37.000Z","dependencies_parsed_at":"2024-11-12T09:38:25.395Z","dependency_job_id":null,"html_url":"https://github.com/adamvvu/tsfracdiff","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adamvvu%2Ftsfracdiff","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adamvvu%2Ftsfracdiff/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adamvvu%2Ftsfracdiff/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adamvvu%2Ftsfracdiff/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/adamvvu","download_url":"https://codeload.github.com/adamvvu/tsfracdiff/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251847846,"owners_count":21653583,"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":["data-science","machine-learning","python","quantitative-finance"],"created_at":"2024-11-12T09:26:53.175Z","updated_at":"2025-05-01T08:33:01.406Z","avatar_url":"https://github.com/adamvvu.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Build](https://img.shields.io/github/actions/workflow/status/adamvvu/tsfracdiff/tsfracdiff_tests.yml?style=for-the-badge)](https://github.com/adamvvu/tsfracdiff/actions/workflows/tsfracdiff_tests.yml)\n[![PyPi](https://img.shields.io/pypi/v/tsfracdiff?style=for-the-badge)](https://pypi.org/project/tsfracdiff/)\n[![Downloads](https://img.shields.io/pypi/dm/tsfracdiff?style=for-the-badge)](https://pypi.org/project/tsfracdiff/)\n[![License](https://img.shields.io/badge/license-MIT-green?style=for-the-badge)](https://github.com/adamvvu/tsfracdiff/blob/master/LICENSE)\n\nEfficient and easy to use fractional differentiation transformations for\nstationarizing time series data in Python.\n\n------------------------------------------------------------------------\n\n## **tsfracdiff**\n\nData with high persistence, serial correlation, and non-stationarity\npose significant challenges when used directly as predictive signals in\nmany machine learning and statistical models. A common approach is to\ntake the first difference as a stationarity transformation, but this\nwipes out much of the information available in the data. For datasets\nwhere there is a low signal-to-noise ratio such as financial market\ndata, this effect can be particularly severe. Hosking (1981) introduces\nfractional (non-integer) differentiation for its flexibility in modeling\nshort-term and long-term time series dynamics, and López de Prado (2018)\nproposes the use of fractional differentiation as a feature\ntransformation for financial machine learning applications. This library\nis an extension of their ideas, with some modifications for efficiency\nand robustness.\n\n[Documentation](https://adamvvu.github.io/tsfracdiff/docs/)\n\n## Getting Started\n\n### Installation\n\n`pip install tsfracdiff`\n\n#### Dependencies:\n\n    # Required\n    python3 # Python 3.7+\n    numpy\n    pandas\n    arch\n\n    # Suggested\n    joblib\n\n### Usage\n\n``` python\n# A pandas.DataFrame/np.array with potentially non-stationary time series\ndf \n\n# Automatic stationary transformation with minimal information loss\nfrom tsfracdiff import FractionalDifferentiator\nfracDiff = FractionalDifferentiator()\ndf = fracDiff.FitTransform(df)\n```\n\nFor a more in-depth example, see this\n[notebook](https://adamvvu.github.io/tsfracdiff/examples/Example.html).\n\n## References\n\nHosking, J. R. M. (1981). Fractional Differencing. Biometrika, 68(1),\n165--176. \u003chttps://doi.org/10.2307/2335817\u003e\n\nLópez de Prado, Marcos (2018). Advances in Financial Machine Learning.\nJohn Wiley \u0026 Sons, Inc.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadamvvu%2Ftsfracdiff","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadamvvu%2Ftsfracdiff","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadamvvu%2Ftsfracdiff/lists"}