{"id":14959573,"url":"https://github.com/artefactory/streamlit_prophet","last_synced_at":"2025-04-04T15:11:56.939Z","repository":{"id":37613766,"uuid":"357924755","full_name":"artefactory/streamlit_prophet","owner":"artefactory","description":"Streamlit app to train, evaluate and optimize a Prophet forecasting 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align=\"center\"\u003e\n\n![Streamlit Prophet](streamlit_prophet/references/logo.png)\n\n[![CI status](https://github.com/artefactory-global/streamlit_prophet/actions/workflows/ci.yml/badge.svg?branch%3Amain\u0026event%3Apush)](https://github.com/artefactory-global/streamlit_prophet/actions/workflows/ci.yml?query=branch%3Amain)\n[![Python Version](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue.svg)](#supported-python-versions)\n[![Dependencies Status](https://img.shields.io/badge/dependabots-active-informational.svg)](https://github.com/artefactory-global/streamlit_prophet/pulls?utf8=%E2%9C%93\u0026q=is%3Apr%20author%3Aapp%2Fdependabot)\n[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://prophet.streamlit.app)\n\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Security: bandit](https://img.shields.io/badge/security-bandit-informational.svg)](https://github.com/PyCQA/bandit)\n[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-informational?logo=pre-commit\u0026logoColor=white)](https://github.com/artefactory-global/streamlit_prophet/blob/main/.pre-commit-config.yaml)\n[![Semantic Versions](https://img.shields.io/badge/%F0%9F%9A%80-semantic%20versions-informational.svg)](https://github.com/artefactory-global/streamlit_prophet/releases)\n[![License](https://img.shields.io/badge/License-MIT-informational.svg)](https://github.com/artefactory-global/streamlit_prophet/blob/main/LICENSE)\n\nDeploy a [Streamlit](https://streamlit.io/) app to train, evaluate and optimize a [Prophet](https://facebook.github.io/prophet/) forecasting model visually\n\n## ⭐  Quick Start  ⭐\n\n[Test the app online](https://prophet.streamlit.app) with shared computing resources \u0026 [read introductory article](https://medium.com/artefact-engineering-and-data-science/visual-time-series-forecasting-with-streamlit-prophet-71d86a769928?source=friends_link\u0026sk=590cca0d24f53f73a9fdb0490a9a47a7)\n\nIf you plan to use the app regularly, you should install the package and run it locally:\n```bash\npip install -U streamlit_prophet\nstreamlit_prophet deploy dashboard\n```\n\n\u003c/div\u003e\n\nhttps://user-images.githubusercontent.com/56996548/126762714-f2d3f3a1-7098-4a86-8c60-0a69d0f913a7.mp4\n\n## 💻 Requirements\n\n### Python version\n* Main supported version : \u003cstrong\u003e3.7\u003c/strong\u003e \u003cbr\u003e\n* Other supported versions : \u003cstrong\u003e3.8\u003c/strong\u003e \u0026 \u003cstrong\u003e3.9\u003c/strong\u003e\n\nPlease make sure you have one of these versions installed to be able to run the app on your machine.\n\n### Operating System\nWindows users have to install [WSL2](https://docs.microsoft.com/en-us/windows/wsl/) to download the package. \nThis is due to an incompatibility between Windows and Prophet's main dependency (pystan). \nOther operating systems should work fine.\n\n## ⚙️ Installation\n\n\n### Create a virtual environment (optional)\nWe strongly advise to create and activate a new virtual environment, to avoid any dependency issue.\n\nFor example with conda:\n```bash\npip install conda; conda create -n streamlit_prophet python=3.7; conda activate streamlit_prophet\n```\n\nOr with virtualenv:\n```bash\npip install virtualenv; python3.7 -m virtualenv streamlit_prophet --python=python3.7; source streamlit_prophet/bin/activate\n```\n\n\n### Install package\nInstall the package from PyPi (it should take a few minutes):\n```bash\npip install -U streamlit_prophet\n```\n\nOr from the main branch of this repository:\n```bash\npip install git+https://github.com/artefactory-global/streamlit_prophet.git@main\n```\n\n\n## 📈 Usage\n\nOnce installed, run the following command from CLI to open the app in your default web browser:\n\n```bash\nstreamlit_prophet deploy dashboard\n```\n\nNow you can train, evaluate and optimize forecasting models in a few clicks.\nAll you have to do is to upload a time series dataset. \nThis dataset should be a csv file that contains a date column, a target column and optionally some features, like on the example below:\n\n![](streamlit_prophet/references/input_format.png)\n\nThen, follow the guidelines in the sidebar to:\n\n* \u003cstrong\u003ePrepare data\u003c/strong\u003e: Filter, aggregate, resample and/or clean your dataset.\n* \u003cstrong\u003eChoose model parameters\u003c/strong\u003e: Default parameters are available but you can tune them.\nLook at the tooltips to understand how each parameter is impacting forecasts.\n* \u003cstrong\u003eSelect evaluation method\u003c/strong\u003e: Define the evaluation process, the metrics and the granularity to\nassess your model performance.\n* \u003cstrong\u003eMake a forecast\u003c/strong\u003e: Make a forecast on future dates that are not included in your dataset,\nwith the model previously trained.\n\nOnce you are satisfied, click on \"save experiment\" to download all plots and data locally.\n\n\n## 🛠️ How to contribute ?\n\nAll contributions, ideas and bug reports are welcome! \nWe encourage you to open an [issue](https://github.com/artefactory-global/streamlit_prophet/issues) for any change you would like to make on this project.\n\n\nFor more information, see [`CONTRIBUTING`](https://github.com/artefactory-global/streamlit_prophet/blob/main/CONTRIBUTING.md) instructions.\nIf you wish to containerize the app, see [`DOCKER`](https://github.com/artefactory-global/streamlit_prophet/blob/main/DOCKER.md) instructions.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fartefactory%2Fstreamlit_prophet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fartefactory%2Fstreamlit_prophet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fartefactory%2Fstreamlit_prophet/lists"}