Time Series Sales Forecasting Project

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Table of Contents

1. Project Scope
2. Audience-Friendly Description
3. Business Impact
4. Project Description
5. Folder Structure
6. Flow Diagram
7. Visualizations & Screenshots
8. Output
9. How to Run & Dependencies
10. Contribution Guidelines
11. License & Credits
12. Next Steps

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1. Project Scope

This project focuses on building an end-to-end predictive pipeline for sales forecasting. Using historical retail data containing 9,800 records, the project implements statistical time series models (ARIMA/SARIMA) to predict future sales trends while accounting for seasonality and underlying data patterns.

2. Audience-Friendly Description

Imagine trying to guess next year's weather by looking at past seasons—this project does exactly that for business sales. It takes years of "messy" sales data, cleans it up, and uses advanced math to draw a "map" of the future. This helps businesses know if they should stock up for a busy month or prepare for a slow one, reducing guesswork and wasted resources.

3. Business Impact

• Inventory Optimization: Forecasts help managers order the right amount of stock, preventing both shortages and overstock costs.
• Financial Planning: Precise revenue projections for the next 12 months allow for better budgeting and investment decisions.
• Risk Mitigation: Confidence intervals provide a "safe zone," showing the best and worst-case scenarios for future revenue.

4. Project Description

The workflow utilizes a rigorous statistical approach to time series analysis:

• Data Ingestion & Cleaning: Processing 18 distinct features, including Order Date, Category, and Sales.
• Exploratory Analysis: Visualizing monthly sales over time and decomposing the series into trend, seasonality, and residuals.
• Stationarity Testing: Utilizing the Augmented Dickey-Fuller (ADF) test to determine if the data needs differencing.
• Automated Model Selection: Using auto_arima to find the optimal $p, d, q$ parameters and seasonal $P, D, Q$ orders.
• Forecasting: Generating a 12-month outlook with multi-level confidence intervals (70%, 80%, and 95%).

5. Folder Structure

├── data/               # Sales dataset (9,800 entries)
├── notebooks/          # EDA and stationarity testing
├── src/                
│   ├── preprocess.py   # Date conversion and monthly aggregation
│   ├── model.py        # ARIMA/SARIMA implementation
│   └── evaluate.py     # Forecasting and plotting confidence intervals
├── output/             # Forecast charts and model summaries
├── requirements.txt    # Library dependencies
└── README.md           # Documentation

6. Flow Diagram

Data Ingestion → Monthly Aggregation → Seasonal Decomposition → ADF Testing → Auto-ARIMA Optimization → 12-Month Forecast

7. Visualizations & Screenshots

Model Comparison: This graph overlays multiple fitting techniques—including Fixed Parameters, Multiplicative Seasonal, and Multiplicative Trend—against the actual historical data. It serves as a visual validation tool to determine which model configuration most accurately mirrors past performance.



Seasonal Decomposition: This multi-panel chart breaks down the raw sales time series into its constituent parts: Trend, Seasonal, and Residual. It allows for the isolation of long-term growth patterns from repeating seasonal cycles and random noise.

Multiplicative Trend Forecast: The upper panel shows a specific forecast utilizing a multiplicative trend model, while the lower panel tracks the residuals over time. Monitoring these residuals is critical to ensure the model has captured all systematic patterns in the data.

8. Output

The project delivers comprehensive visual insights into future performance:

Sales Forecast with Confidence Intervals

The final output provides a specific "Final Forecast" value alongside shaded regions representing different levels of statistical certainty (70%, 80%, and 95% Confidence Intervals).

Seasonal Decomposition

Breaks down the raw sales data into its core components: the overall Trend, repeating Seasonal patterns, and the Residual noise.

9. How to Run & Dependencies

Install Requirements

pip install pandas numpy statsmodels matplotlib pmdarima
python src/model.py

10. Contribution Guidelines

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/NewAlgorithm).
3. Commit your changes.
4. Push to the branch and open a Pull Request.

11. License & Credits

• License: Distributed under the MIT License.
• Credits: Statistical methods powered by the statsmodels and pmdarima libraries.

12. Next Steps

• Data Versioning: Integrate DVC (Data Version Control) to track dataset changes.
• Model Deployment: Deploy the model as a REST API using FastAPI.
• Automation: Automate the workflow with GitHub Actions for CI/CD.