{"id":22379120,"url":"https://github.com/sky-uk/anticipy","last_synced_at":"2025-05-11T01:15:57.407Z","repository":{"id":50838877,"uuid":"148176393","full_name":"sky-uk/anticipy","owner":"sky-uk","description":"A Python library for time series forecasting","archived":false,"fork":false,"pushed_at":"2023-01-18T17:54:07.000Z","size":356,"stargazers_count":81,"open_issues_count":43,"forks_count":14,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-03-31T21:51:20.034Z","etag":null,"topics":["forecasting","python","regression","time-series"],"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/sky-uk.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-09-10T15:22:25.000Z","updated_at":"2025-03-22T08:21:05.000Z","dependencies_parsed_at":"2023-02-10T16:25:12.914Z","dependency_job_id":null,"html_url":"https://github.com/sky-uk/anticipy","commit_stats":null,"previous_names":[],"tags_count":11,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sky-uk%2Fanticipy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sky-uk%2Fanticipy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sky-uk%2Fanticipy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sky-uk%2Fanticipy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sky-uk","download_url":"https://codeload.github.com/sky-uk/anticipy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252145016,"owners_count":21701439,"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":["forecasting","python","regression","time-series"],"created_at":"2024-12-04T23:09:00.948Z","updated_at":"2025-05-11T01:15:57.381Z","avatar_url":"https://github.com/sky-uk.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Latest Release](https://img.shields.io/pypi/v/anticipy.svg)](https://img.shields.io/pypi/v/anticipy.svg)\n[![Build Status](https://travis-ci.com/sky-uk/anticipy.svg?branch=master)](https://travis-ci.com/sky-uk/anticipy)\n[![Documentation Status](https://readthedocs.org/projects/anticipy/badge/?version=latest)](https://anticipy.readthedocs.io/en/latest/?badge=latest)\n[![Code Coverage](https://codecov.io/github/sky-uk/anticipy/branch/master/graph/badge.svg)](https://codecov.io/github/sky-uk/anticipy/)\n[![pulls](https://img.shields.io/docker/pulls/skyuk/anticipy.svg)](https://hub.docker.com/r/skyuk/anticipy)\n\n\n\n# Anticipy\n\nAnticipy is a tool to generate forecasts for time series. It takes a pandas Series or DataFrame as input, and\nreturns a DataFrame with the forecasted values for a given period of time.\n\nFeatures:\n\n* **Simple interface**. Start forecasting with a single function call on a pandas DataFrame.\n* **Model selection**. If you provide different multiple models (e.g. linear, sigmoidal, exponential), the tool will\n  compare them and choose the best fit for your data.\n* **Trend and seasonality**. Support for weekly and monthly seasonality, among other types.\n* **Calendar events**. Provide lists of special dates, such as holiday seasons or bank holidays, to improve model\n  performance.\n* **Data cleaning**. The library has tools to identify and remove outliers, and to detect and handle step changes in\n  the data.\n\nIt is straightforward to generate a simple linear model with the tool - just call forecast.run_forecast(my_dataframe):\n\n```python\n   import pandas as pd, numpy as np\n   from anticipy import forecast\n   \n   df = pd.DataFrame({'y': np.arange(0., 5)}, index=pd.date_range('2018-01-01', periods=5, freq='D'))\n   df_forecast = forecast.run_forecast(df, extrapolate_years=1)\n   print(df_forecast.head(12))\n```\n\nOutput:\n\n```\n   .        date   model             y  is_actuals\n   0  2018-01-01       y  0.000000e+00        True\n   1  2018-01-02       y  1.000000e+00        True\n   2  2018-01-03       y  2.000000e+00        True\n   3  2018-01-04       y  3.000000e+00        True\n   4  2018-01-05       y  4.000000e+00        True\n   5  2018-01-01  linear  5.551115e-17       False\n   6  2018-01-02  linear  1.000000e+00       False\n   7  2018-01-03  linear  2.000000e+00       False\n   8  2018-01-04  linear  3.000000e+00       False\n   9  2018-01-05  linear  4.000000e+00       False\n   10 2018-01-06  linear  5.000000e+00       False\n   11 2018-01-07  linear  6.000000e+00       False\n```\n\n\nDocumentation is available in [Read the Docs](https://anticipy.readthedocs.io/en/latest/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsky-uk%2Fanticipy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsky-uk%2Fanticipy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsky-uk%2Fanticipy/lists"}