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This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

License: MIT License

Python 95.15% Dockerfile 4.85%
steamlit streamlit-dashboard python fbprophet machine-learning forecasting forecasting-models forecasting-model time-series

streamlit_forecasting_app's Introduction

๐Ÿ“ˆ Automated Time Series Forecasting

Background: This MVP data web app uses the Streamlit framework and Facebook's Prophet forecasting package to generate a dynamic forecast from your own data.

Try it out here: https://share.streamlit.io/zachrenwick/streamlit_forecasting_app/app.py

You'll be able to import your data from a CSV file, visualize trends and features, analyze forecast performance, and finally download the created forecast ๐Ÿ˜ต

In beta mode

Created by Zach Renwick: https://twitter.com/zachrenwick.

Code available here: https://github.com/zachrenwick/streamlit_forecasting_app

Screenshot1 Screenshot2 Screenshot3 Screenshot4

Docker

  • Build with docker build -t ts-forecast-app . (takes some time!)
  • Run with docker run -p 8501:8501 ts-forecast-app:latest
  • Open http://localhost:8501/

Example data

The Peyton Manning data from the prophet quickstart is included in the example_data folder

streamlit_forecasting_app's People

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