Rolling Revenue Forecasting

This workflow builds, validates, and deploys a rolling revenue forecasting model that predicts and recursively updates monthly revenue predictions using historical financial data. After reading the data, it engineers time-based features, trains a Random Forest regression model, and generates rolling forecasts that simulate how predictions would evolve as new data becomes available.

• Train and apply a regression model for revenue forecasting
• Capture temporal revenue patterns using lag-based feature engineering
• Use time-aware validation to ensure realistic model evaluation
• Generate rolling, walk-forward forecasts that reflect real-world financial forecasting processes

After accessing historical data on financial performance, the workflow engineers lag-based features to represent past revenue values across multiple time windows. These features allow the model to learn from historical revenue dynamics and capture recurring patterns and trends.

The data is split chronologically into training and test sets. Historical data is used to train a Random Forest regression model, while a reserved test set is used to evaluate how well the model performs on unseen data, providing a realistic measure of predictive performance.

In the deployment workflow, the trained model is applied to generate forecasts across regions and product lines using a dynamic walk-forward approach that reflects real-world monthly forecasting. In this scenario, predictions are recursively fed back into the dataset and the model to generate sequential forecasts for future periods.

Historical and forecasted values are combined into a unified dataset, enabling clear comparison between past and future revenue. Interactive dashboards and charts allow users to explore projections by product line or region and analyze total revenue trends.