Comparing Statistical and ML Forecasting on Real Sales Data
📰 Medium · Data Science
Compare statistical and ML forecasting on real sales data to determine which approach is more effective and why it matters for accurate predictions
Action Steps
- Run a statistical time series model on retail sales data to establish a baseline
- Implement machine learning models such as Random Forest and XGBoost with lag features on the same data
- Compare the performance of the statistical and ML models using metrics such as error and volatility
- Evaluate the ability of each model to capture seasonality in the data
- Refine the ML models by adjusting lag features and hyperparameters to improve performance
Who Needs to Know This
Data scientists and analysts can benefit from this comparison to choose the best forecasting approach for their retail sales data, and product managers can use the insights to inform business decisions
Key Insight
💡 Machine learning models require careful feature engineering, such as lag features, to effectively capture time-based patterns in data
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Machine learning models don't always outperform statistical models in forecasting retail sales data. Find out why and how to improve ML model performance #machinelearning #forecasting
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