This tutorial demonstrates how to perform advanced statistical analysis and predict future data using the SARIMA model in Python, without writing extensive code.
Key Steps:
- Data Import:
- Import daily sales data from Google Sheets into Google Colab using AI-generated Python code.
- Statistical Analysis:
- Utilize AI to perform and visualize key statistical analyses, including:
- Augmented Dickey-Fuller test for stationarity
- Trend and seasonality decomposition
- Autocorrelation (ACF) and Partial Autocorrelation (PACF) plots
- Utilize AI to perform and visualize key statistical analyses, including:
- SARIMA Model Creation:
- Employ AI to generate Python code for creating a SARIMA model based on the analyzed data.
- Future Sales Prediction:
- Predict sales for the next 30 days using the generated SARIMA model.
- Result Visualization:
- Present the predicted sales data in both tabular and graphical formats for easy interpretation.
Key Takeaways:
- This tutorial highlights the power of AI in simplifying data analysis and machine learning tasks.
- By leveraging AI-generated code, users can efficiently perform complex statistical analyses and build predictive models without deep Python programming knowledge.
- The focus on the SARIMA model provides a practical example of how to forecast time series data effectively.
Google Sheets:
https://docs.google.com/spreadsheets/d/1Vphx5JGhDGR9YP4Zh69r4Hb3fHAY2_fG4loylzCVhBs/edit?usp=sharing
Google Colab Notebook:
https://colab.research.google.com/drive/1ba4Ef0fUeiP5UI0mRsyQo-PhMjn4l7_H?usp=sharing