Demand Prediction Model Selection for Data Scarcity
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Solution Overview
Problem
Existing demand prediction models generated by AI struggle to maintain high accuracy regardless of the amount of learning data, particularly when dealing with new products or limited data scenarios.
Innovation Solution
A demand prediction device that acquires both single article and category prediction models, calculates their accuracy, and adopts the most accurate model for demand prediction, ensuring enhanced prediction accuracy regardless of the learning data amount.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single article prediction model is used to predict demand of a target product, then the prediction can be performed for specific products, but the accuracy deteriorates when the amount of learning data is small
Solution Approach 1:
The patent combines single article prediction models with category prediction models to create an integrated prediction system. When learning data for a specific product is insufficient, the system merges the product's prediction model with its category's prediction model, allowing the category data to supplement the product-specific data and maintain prediction accuracy even with limited learning data
Solution Approach 2:
The patent creates a multi-functional prediction system that can operate in multiple modes: using only single article models, using only category models, or combining both. This universal approach allows the system to adapt to different data availability scenarios and maintain functionality across various product types and data conditions
2Measurement precision
If a category prediction model is used to predict demand of a category including the target product, then the prediction can be performed with limited data, but the accuracy deteriorates compared to single article models when sufficient data is available
Solution Approach 1:
The patent implements a dynamic model selection mechanism that automatically adjusts which prediction model to use based on the available learning data. The system evaluates data sufficiency metrics and dynamically switches between single article models, category models, or combined models, making the system adaptable to varying data conditions rather than being fixed to one approach
3Measurement precision
If the prediction model is changed according to data amount of past sales results, then the prediction accuracy can be improved when data amount increases, but the system complexity increases
Solution Approach 1:
The patent implements a self-service model evaluation and selection system that automatically assesses the performance of different prediction models and selects the most appropriate one without requiring complex external intervention. The system autonomously evaluates accuracy metrics, data sufficiency, and model performance to make selection decisions, reducing the operational complexity despite having multiple models
Data Source
AI summary
A demand prediction device of the present disclosure includes a model acquisition means that acquires a single article prediction model that predicts a demand of a target product and a category prediction model that predicts a demand of a category including the target product, a calculation means that calculates accuracy of demand prediction of the target product in the single article prediction model and the category prediction model, and an adoption means that adopts any one of the single article prediction model and the category prediction model based on the accuracy.