metadata

title: Time Series Autocorrelation Demo
emoji: 📈
colorFrom: indigo
colorTo: blue
sdk: streamlit
sdk_version: 1.21.0
app_file: app.py
pinned: false
license: openrail

Time series autocorrelation tool

Tool demonstrating time series autocorrelation analysis with Python

Assumes uploaded data is clean.

Built With

Streamlit

Local setup

Obtain the repo locally and open its root folder

To potentially contribute

git clone https://github.com/pkiage/tool-time-series-autocorrelation-demo

gh repo clone pkiage/tool-time-series-autocorrelation-demo

Just to deploy locally

Download ZIP

(optional) Setup virtual environment:

python -m venv venv

(optional) Activate virtual environment:

If using Unix based OS run the following in terminal:

.\venv\bin\activate

If using Windows run the following in terminal:

.\venv\Scripts\activate

Install requirements by running the following in terminal:

Required packages

pip install -r requirements.txt

Build and install local package

python setup.py build

python setup.py install

Run the streamlit app (app.py) by running the following in terminal (from repository root folder):

streamlit run src/app.py

Project structure based on the cookiecutter data science project template.

Hugging Face Tips

Initial Setup

When creating the Spaces Configuration Reference ensure the Streamlit Space version (sdk_version) specified is supported by HF

git remote add space https://huggingface.co/spaces/pkiage/time_series_autocorrelation_demo

git push --force space main

When syncing with Hugging Face via Github Actions the User Access Token created on Hugging Face (HF) should have write access

Demo Links

Hugging Face Space: https://huggingface.co/spaces/pkiage/time_series_autocorrelation_demo
Streamlit Community Cloud: https://pkiage-tool-time-series-autocorrelation-demo-app-l0umps.streamlit.app/