{"id":28384229,"url":"https://github.com/k-ibadullaev/py-ssa-lib","last_synced_at":"2026-05-10T02:36:28.568Z","repository":{"id":252090222,"uuid":"835918546","full_name":"K-Ibadullaev/py-ssa-lib","owner":"K-Ibadullaev","description":"Py-SSA-Lib: Python  implementation of the multichannel singular spectrum analysis (MSSA) and  singular spectrum analysis (SSA)","archived":false,"fork":false,"pushed_at":"2024-12-12T11:12:52.000Z","size":9855,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-05-30T08:53:21.070Z","etag":null,"topics":["mssa","numpy","python","singular-spectrum-analysis","ssa","time-series","time-series-analysis","time-series-forecasting"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/K-Ibadullaev.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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,"zenodo":null}},"created_at":"2024-07-30T19:34:58.000Z","updated_at":"2025-03-04T21:15:01.000Z","dependencies_parsed_at":null,"dependency_job_id":"a7d221c9-806c-4393-8d02-997a0f2802e4","html_url":"https://github.com/K-Ibadullaev/py-ssa-lib","commit_stats":null,"previous_names":["k-ibadullaev/py_ssa","k-ibadullaev/py-ssa-lib"],"tags_count":8,"template":false,"template_full_name":null,"purl":"pkg:github/K-Ibadullaev/py-ssa-lib","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/K-Ibadullaev%2Fpy-ssa-lib","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/K-Ibadullaev%2Fpy-ssa-lib/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/K-Ibadullaev%2Fpy-ssa-lib/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/K-Ibadullaev%2Fpy-ssa-lib/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/K-Ibadullaev","download_url":"https://codeload.github.com/K-Ibadullaev/py-ssa-lib/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/K-Ibadullaev%2Fpy-ssa-lib/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261972724,"owners_count":23238538,"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":["mssa","numpy","python","singular-spectrum-analysis","ssa","time-series","time-series-analysis","time-series-forecasting"],"created_at":"2025-05-30T08:08:32.213Z","updated_at":"2026-05-10T02:36:28.306Z","avatar_url":"https://github.com/K-Ibadullaev.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PY-SSA-LIB PACKAGE\n## Intro\nWelcome to the page of the **py-ssa-lib** package!\nThis package contains python implementations of the  **Singular Spectrum Analysis(SSA)** and **Multichannel Singular Spectrum Analysis(MSSA)**. \n\nIt can be used for the time series analysis and forecasting.\n\n**Please, take a look on the guides for SSA and MSSA which are available in the [corresponding directory](https://github.com/K-Ibadullaev/py_ssa/tree/main/examples_and_guide) !**\n\n## Mathematical Background\nThe [Wiki](https://github.com/K-Ibadullaev/py-ssa-lib/wiki) for the **py-ssa-lib** package is now available and will be periodically updated. It contains some theoretical background about the MSSA and SSA.\n\n## Updates\n**NEW:** The bootstrap prediction intervals are now available both for MSSA and SSA( and other models from other packages, since it is model free).\nJust import **bootstrap_prediction_intervals** from the new module **tools** and pass residuals and forecasted values.\n\n \n## Installation\n```shell\n$ python -m pip install py-ssa-lib\n```\n\n## Requirements\nThe required packages are listed in the requirements.txt and can be installed from this file via pip.\n\nAll dependencies should be automatically installed during the installation of the **py-ssa-lib**\n\nThe classes in the **py-ssa-lib** heavily rely on the numpy, scipy, sklearn, pandas and matplotlib libraries.\n\n## Similar Python Packages\nBefore the development of the **py-ssa-lib** I searched for the \nthe Python packages which implement both MSSA and SSA, and found only few decent packages with the similar functionality:\n\n- https://github.com/AbdullahO/mSSA?tab=readme-ov-file\n- https://github.com/kieferk/pymssa\n  \nHowever, they seem to be no longer maintained and they provided a limited functionality in comparison to what I need.\n(Arguably) the best functionality is provided by the RSSA-package in R (https://github.com/asl/rssa).\nSo the aim of this package is to migrate the most useful functions from the RSSA-package into Python, in order to provide a seamless workflow for the time series analysis.\n\n\n## List of the Core Packages\n- NumPy https://numpy.org/\n- SciPy https://scipy.org/\n- Jupyter Lab https://jupyterlab.readthedocs.io/en/latest/index.html\n- Scikit-learn https://scikit-learn.org/stable/\n- Matplotlib https://matplotlib.org/\n\n\n  \n## Literature about SSA and MSSA\n- https://www.kaggle.com/code/jdarcy/introducing-ssa-for-time-series-decomposition/notebook#2.-Introducing-the-SSA-Method\n- https://link.springer.com/book/10.1007/978-3-642-34913-3\n- https://link.springer.com/book/10.1007/978-3-662-57380-8\n- https://www.gistatgroup.com/gus/mssa2.pdf\n  \n\n\n\n### Citation \nIf you find this package useful, please, cite:\n\n**Konstantin Ibadullaev, https://github.com/K-Ibadullaev/py_ssa-lib/**\n\n(This file and the citation format will change over time.)\n\n\n\n\n### Acknowledgements\nThis package is developed as a part of the research project \"Intelligent Geosystems\" (100693905) supported by ESF funding\n\n![alt text](https://github.com/K-Ibadullaev/py-ssa-lib/blob/main/ESFICON.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fk-ibadullaev%2Fpy-ssa-lib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fk-ibadullaev%2Fpy-ssa-lib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fk-ibadullaev%2Fpy-ssa-lib/lists"}