Demand Forecasting Models Using Synthetic Data and Auto-Tuning
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Solution Overview
Problem
Existing demand forecasting systems face challenges such as scarce data availability, flawed training data, difficulty in identifying appropriate models or parameters, and challenges in forecasting demand for numerous items or across different channels, especially when item catalogs change.
Innovation Solution
A demand forecasting system utilizing computer models that include components for model training, synthetic data generation, feature management, hyperparameter tuning, and AI integration to improve forecasting accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting models are used with limited training data, then model training is simpler, but forecasting accuracy deteriorates due to data scarcity and flawed training data
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data before actual model training occurs. This synthetic data is created to supplement scarce real data, ensuring that models have sufficient training material even when real-world data is limited or flawed.
Solution Approach 2:
Synthetic data acts as an intermediary between the scarcity of real data and the requirements of model training. It bridges the gap by providing additional training examples that mimic real data patterns without requiring actual real-world data for every training instance.
2Measurement precision
If multiple models and parameters are evaluated for different items, then forecasting accuracy improves, but system complexity increases making it challenging to identify appropriate models
Solution Approach 1:
The system implements feedback mechanisms that automatically evaluate model performance and provide guidance on model selection. This feedback loop helps identify appropriate models for different items without requiring manual intervention, reducing the complexity of model selection while maintaining accuracy.
Solution Approach 2:
The system enables self-service by allowing models to automatically select and adapt to appropriate forecasting approaches for different items. The system autonomously identifies which models work best for specific items without requiring external expertise or manual configuration.
3Adaptability or versatility
If forecasting is performed across hundreds or thousands of items with changing catalogs, then comprehensive coverage is achieved, but processing time and computational resources increase
Solution Approach 1:
The system segments the large-scale forecasting problem into smaller, manageable components. By dividing the item catalog into groups and applying forecasting models at appropriate levels of granularity, the system can handle thousands of items efficiently without requiring all items to be processed uniformly, thus reducing overall processing time.
Solution Approach 2:
The system creates universal forecasting capabilities that can handle diverse item catalogs through multi-functional models. These models are designed to adapt to different item types and catalog changes, providing comprehensive coverage while maintaining efficient processing through standardized approaches that work across multiple contexts.
4Measurement precision
If channel-specific forecasting models are developed for different sales channels, then forecasting accuracy across channels improves, but system complexity and data management burden increase
Solution Approach 1:
The system employs universal forecasting models that can operate across multiple sales channels simultaneously. These multi-functional models are designed to handle channel-specific characteristics without requiring separate dedicated models for each channel, thus improving multi-channel forecasting accuracy while controlling system complexity.
Solution Approach 2:
The system merges channel-specific forecasting requirements into a unified modeling framework. By combining data and models across channels while preserving channel-specific patterns, the system achieves accurate multi-channel forecasting without the complexity of completely separate systems for each channel.
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.