Promotional Forecasting System Using ML-AI Segmentation

The patent segments the forecasting problem by creating separate models for different product categories and retail locations. Each model is trained on specific data relevant to its segment, allowing the system to handle complexity through organized division rather than monolithic processing. This segmentation enables accurate local forecasting while maintaining manageable system architecture through modular design.

The patent introduces multiple dimensions to the forecasting approach by incorporating promotional indicators, geographical location data, and temporal patterns into the model inputs. This dimensional expansion allows the system to capture complex relationships between promotions, location characteristics, and demand patterns, improving accuracy without overwhelming complexity by organizing data structurally.

The patent performs preliminary data processing and model training in advance, creating pre-trained models that can quickly generate forecasts for new locations or products. Historical data is processed and models are trained beforehand, so when actual forecasting is needed, the system can leverage these pre-computed models rather than processing everything from scratch, significantly reducing real-time processing time while maintaining local precision.

The patent creates copies of proven models and adapts them to new locations or product categories rather than training entirely new models from scratch. This model copying approach allows the system to leverage existing knowledge and quickly adjust to local variations, reducing computational time while maintaining forecast precision through targeted model adaptation rather than retraining.

The patent segments the forecasting system into separate, category-specific and location-specific models. This segmentation allows the system to handle multiple product categories and locations simultaneously by processing them as distinct units, each with its own trained model. The modular structure manages complexity through organization, enabling versatile forecasting across diverse scenarios without creating a single complex monolithic model.

The patent creates a universal forecasting framework that can handle multiple product categories and locations through a common architectural structure. The same basic model framework and data processing pipeline serve all categories and locations, with specific models instantiated as needed. This universality enables versatile multi-location and multi-category forecasting while managing complexity through standardized, reusable components rather than unique models for each scenario.