pkiage/time_series_autocorrelation_demo

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

or

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

<p><small>Project structure based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>.</small></p>

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/timeseriesautocorrelation_demo
• —Streamlit Community Cloud: https://pkiage-tool-time-series-autocorrelation-demo-app-l0umps.streamlit.app/