Retail Sales Forecasting
Using Advanced Time Series Techniques
This project leverages cutting-edge time series forecasting techniques to predict monthly retail sales. It integrates hyperparameter tuning, cross-validation, and feature engineering to deliver highly accurate and interpretable results. The results provide actionable insights for inventory management, revenue forecasting, and strategic planning.
The Challenge
- Accurate sales forecasting is critical for retail businesses. The challenge lies in capturing seasonal trends, holiday effects, and unexpected changes in demand using historical sales data.
- Objective: Develop a robust forecasting model capable of predicting monthly retail sales and identifying trends to aid decision-making.
Methodology and Tools
- Feature Engineering: Extracted key date-based features (e.g., year, month, day of the week, and weekend indicators).
- Forecasting: Used Meta’s Prophet library for modeling with hyperparameter tuning for seasonal components.
- Cross-Validation: Implemented time-series split for robust model evaluation.
- Evaluation Metrics: Evaluated accuracy using MAE and RMSE.
- Python, Prophet, pandas, matplotlib, scikit-learn, Streamlit (for deployment).
- Display as icons (e.g., Python, Meta’s logo, Streamlit).
RESULT
Actual vs Predicted sales with confidence intervals.
We predicted future sales using past data. The chart shows how close predictions are to actual sales, with accuracy results. This helps businesses plan better for inventory, budgets, and sales goals.
Key Insights
occur around holidays.
identified post-pandemic in retail stores.
Professional Enhancements
What Makes This Project Stand Out
Hyperparameter Tunning
Hyperparameter tuning for seasonality (additive vs. multiplicative) and changepoint detection.
Feature Engineering
Advanced feature engineering integrating external regressors (e.g., holidays).
Cross-Validation
Cross-validation for robust evaluation and interpretability.
Interactive Dashboard
Deployment-ready dashboard for interactive forecasting.
Optimization
Future Enhancements
External Regressors
Include additional external regressors like promotions and economic factors.
Multi-Store Level
Extend the forecast to multi-store-level predictions.
Data Ingestion
Automate data ingestion and model retraining pipeline.
Projects
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