Metadata-Version: 2.1
Name: ts_app
Version: 0.0.2
Summary: A simple dashboard app to interactively fit ARIMA models.
Home-page: https://github.com/Tim-Abwao/time-series-app
Author: Abwao
Author-email: abwaomusungu@gmail.com
License: MIT
Description: # Time Series App
        
        A simple web app to learn a little about *[Time Series][1] analysis* and *forecasting*.
        
        You can create a sample, or upload a file, and interactively fit a time series model on it. To give it a try, [click here...][2]
        
        ![screencast of the app](dashboard.gif)
        
        ## Installation
        
        The easiest way to install the app is from [PyPI][3] using:
        
        ```bash
        pip install ts_app
        ```
        
        ## Manual set up
        
        ### 1. Using a virtual environment
        
        You'll need [Python][4] 3.8 and above. Packages used include [statsmodels][5], [flask][6], [dash][7], [pandas][8] and [NumPy][9].
        
        1. Fetch the necessary files:
        
            ```bash
            git clone https://github.com/Tim-Abwao/time-series-app.git
            cd time-series-app
            ```
        
        2. Create the virtual environment:
        
            ```bash
            python3 -m venv venv
            source venv/bin/activate
            pip install -U pip
            pip install -r requirements.txt
            ```
        
        3. Start the app:
        
            You can use the convenient `run.sh` script:
        
            ```bash
            bash run.sh
            ```
        
            then browse to [localhost:8000](http://127.0.0.1:8000) to interact with the web app.
        
            Afterwards, use `CTRL` + `C` to stop it.
        
        ### 2. Using Docker
        
        You'll need [Docker][10].
        
        1. Fetch the necessary files, just as above:
        
            ```bash
            git clone https://github.com/Tim-Abwao/time-series-app.git
            cd time-series-app
            ```
        
        2. Build an image for the app and run it in a container,
        
            ```bash
            docker build --tag ts_app .
            docker run --name ts -d -p 8000:8000 --rm  ts_app
            ```
        
            in which case the app will be running at <http://0.0.0.0:8000>.
        
            Afterwards, use
        
            ```bash
            docker stop ts
            ```
        
            to terminate it.
        
        [1]: https://en.wikipedia.org/wiki/Time_series
        [2]: https://time-series-app.herokuapp.com
        [3]: https://pypi.org/
        [4]: https://www.python.org "The Python programming language"
        [5]: https://www.statsmodels.org/stable/index.html
        [6]: https://flask.palletsprojects.com/en/1.1.x/
        [7]: https://dash.plotly.com/
        [8]: https://pandas.pydata.org
        [9]: https://numpy.org
        [10]: https://www.docker.com/
        
Keywords: time_series dashboard ARIMA
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Framework :: Dash
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
