{"id":25053901,"url":"https://github.com/benman1/python-time-series","last_synced_at":"2026-02-22T09:02:48.067Z","repository":{"id":90551589,"uuid":"581486398","full_name":"benman1/python-time-series","owner":"benman1","description":"Time-Series analysis, statistical and machine learning models for forecasting, regression, and classification","archived":false,"fork":false,"pushed_at":"2024-12-02T14:29:18.000Z","size":11859,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-04-14T09:57:40.903Z","etag":null,"topics":["darts","deep-learning","forecasting","mlforecast","nixtla","scikit-learn","statsforecast","time-series","time-series-analysis"],"latest_commit_sha":null,"homepage":"https://amzn.to/4iiFWWa","language":"Jupyter Notebook","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/benman1.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":"2022-12-23T10:27:44.000Z","updated_at":"2024-12-02T14:30:43.000Z","dependencies_parsed_at":"2025-02-06T11:55:47.280Z","dependency_job_id":"61137db1-072f-4da5-b85a-ad4fde71d5b0","html_url":"https://github.com/benman1/python-time-series","commit_stats":{"total_commits":55,"total_committers":2,"mean_commits":27.5,"dds":"0.054545454545454564","last_synced_commit":"649cbca72895389005865ee3e40257ba89b0d5f2"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/benman1/python-time-series","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benman1%2Fpython-time-series","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benman1%2Fpython-time-series/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benman1%2Fpython-time-series/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benman1%2Fpython-time-series/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/benman1","download_url":"https://codeload.github.com/benman1/python-time-series/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benman1%2Fpython-time-series/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29707521,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-22T05:59:28.568Z","status":"ssl_error","status_checked_at":"2026-02-22T05:58:46.208Z","response_time":110,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["darts","deep-learning","forecasting","mlforecast","nixtla","scikit-learn","statsforecast","time-series","time-series-analysis"],"created_at":"2025-02-06T11:55:40.903Z","updated_at":"2026-02-22T09:02:48.029Z","avatar_url":"https://github.com/benman1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"This is the code repository for \u003ca href =\"https://amzn.to/4iiFWWa\"\u003eMachine Learning for Time Series\u003c/a\u003e, published by Packt.\u003c/p\u003e\n\n\n# python-time-series\nTime-Series analysis, statistical and machine learning models for forecasting, regression, and classification\n\n\n## Environment\nYou can install your local environment with conda (recommended) or pip. The environment configurations for conda and pip are provided. Please note that if you choose pip as you installation tool, you might need additional tweaking.\n\nIf you have any problems with the environment, please raise an issue, where you show the error you got. If you feel confident, please go ahead and create a pull request.\n\n### Conda\nThis is the recommended method for installing dependencies. Please make sure you have [anaconda](https://www.anaconda.com/download) installed.\n\nFirst create the environment for the book that contains all the dependencies:\n```bash\nconda env create --file time_series.yml\n```\n\nThe conda environment is called `time_series`. You can activate it as follows:\n```bash\nconda activate time_series\n```\n\n### Pip\n[Pip](https://pypi.org/project/pip/) is the default dependency management tool in Python. With pip, you should be able to install all the libraries from the requirements file:\n\n```bash\npip install -r requirements.txt\n```\n\n### Docker\nThere's a [docker](https://www.docker.com/) file for the environment as well. It uses the docker environment and starts an ipython notebook. To use it, first build it, and then run it:\n\n```bash\ndocker build -t new_image .\ndocker run -it new_image\n```\n\nYou should be able to find the notebook in your browser at [http://localhost:8080](http://localhost:8080).\n\n### Poetry\n\nMake sure you have [poetry](https://python-poetry.org/) installed. On Linux and MacOS, you should be able to use the requirements file:\n```bash\npoetry init\ncat requirements.txt | xargs poetry add\n```\n\n## Contributing\n\nIf you find anything amiss with the notebooks or dependencies, please feel free to create a pull request.\n\nIf you want to change the conda dependency specification (the yaml file), you can test it like this:\n```bash\nconda env create --file time_series.yml --force\n```\n\nYou can update the pip requirements like this:\n```bash\npip freeze \u003e requirements.txt\n```\n\nPlease make sure that you keep these two ways of maintaining dependencies in sync.\n\nThen make sure, you test the notebooks in the new environment to see that they run.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenman1%2Fpython-time-series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbenman1%2Fpython-time-series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenman1%2Fpython-time-series/lists"}