Arauto

Abstract

1. Introduction

Arauto is an open-source framework that aims to make it easier to model and experiment time series analysis and forecasting. Arauto offers a intuitive and interactive interface to explore different parameters for models using Autoregressive models (AR, ARMA, ARIMA, SARIMA, ARIMAX, and SARIMAX). More estimators and algorithms are on the way.

2. Objective

An open-source tool for quick Time Series Analysis and Forecasting

This project demonstrates how Python can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Support for exogenous regressors (independent variables)
• Seasonal decompose that lets you know the Trend, Seasonality, and Resid of your data
• Stationarity Test using Augmented Dickey-Fuller test
• Customization of data transforming for stationarity: you can use from first difference to seasonal log to transform your data
• ACF (Autocorrelation function) and PACF (Parcial correlation function) for terms estimation
• Customize ARIMA terms or let Arauto choose the best for you based on your data
• Grid search feature for parameters tuning
• Code generation: at the end of the process, Arauto returns the code used to transform the data and train the model

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/paulozip/arauto.git
cd arauto

# Clone the repository
git clone https://github.com/paulozip/arauto.git
cd arauto

# If you're using Anaconda
conda create --name arauto_env
conda activate arauto_env

# Install dependencies
pip install -r requirements.txt

# Run Streamlit
streamlit run run.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by paulozip and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.