Demand Forecast Feature Selection Under Sparse Item Data
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Solution Overview
Problem
Computer models face challenges in forecasting item demand due to scarce data, flawed training data, model inappropriateness for specific items, and difficulty in handling item catalog changes and channel variations.
Innovation Solution
A demand forecasting system utilizing computer models that include model training, synthetic data generation, feature management, hyperparameter tuning, and AI integration to improve forecasting accuracy and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a model is trained using available historical data, then the model can be developed for forecasting, but the forecast accuracy deteriorates when data is scarce or flawed
Solution Approach 1:
The patent introduces an intermediary feature selection process that acts as a mediator between the available historical data and the forecasting model. This feature selection component identifies and extracts only the most relevant features from the available data, even when data quantity is limited, thereby improving forecast accuracy without requiring more data.
Solution Approach 2:
The patent dynamically changes model parameters and feature sets based on the characteristics of available data. When data is scarce or flawed, the system adjusts which features are included in the model and modifies model parameters to optimize performance under data constraints, rather than using a fixed model configuration.
2Ease of operation
If a single forecasting model is used for all items, then the system is simple to operate, but the model may be inappropriate for specific items with different demand patterns
Solution Approach 1:
The patent implements a dynamic model selection and configuration system that automatically adapts the forecasting approach to each item's characteristics. Rather than using a static single model for all items, the system dynamically selects and configures models based on item-specific demand patterns, making the system both versatile and relatively easy to operate through automation.
Solution Approach 2:
The patent applies different model configurations and feature sets to different items based on their local characteristics and demand patterns. Each item receives a customized forecasting approach tailored to its specific needs, rather than applying a uniform model across all items, thereby improving model appropriateness while maintaining operational simplicity through automated differentiation.
3Adaptability or versatility
If the forecasting system handles hundreds or thousands of items with changing catalogs, then comprehensive coverage is achieved, but the complexity of managing and updating models increases
Solution Approach 1:
The patent segments the large item catalog into smaller groups or categories with similar characteristics, and applies appropriate model configurations to each segment. This segmentation approach allows the system to handle hundreds or thousands of items comprehensively while reducing management complexity by dealing with items in manageable groups rather than as a monolithic system.
Solution Approach 2:
The patent develops a universal feature selection and model configuration framework that can handle diverse item types and catalog changes through a single unified system. This multi-functional approach allows the same core system to adapt to different item categories and catalog structures, achieving comprehensive coverage without proportionally increasing management complexity.
4Measurement precision
If features are selected manually for each item, then model accuracy can be optimized, but the time and resources required increase significantly
Solution Approach 1:
The patent implements a self-service feature selection system that automatically identifies and selects relevant features for each item without requiring manual intervention. The system uses automated algorithms to analyze item characteristics and select appropriate features, achieving high feature selection accuracy while eliminating the significant time and resource costs associated with manual feature selection for each item.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from past forecasting performance and automatically adjusts feature selection based on what has worked well for similar items. This feedback-driven approach enables the system to achieve high feature selection accuracy automatically, reducing both time and resources compared to manual feature selection while maintaining or improving precision.
Data Source
AI summary
Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.