Champion Challenger Model Selection for Dynamic Forecasting
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Solution Overview
Problem
Current data analytics systems lack an efficient mechanism for automatically selecting the most appropriate model in response to changing data patterns and fluctuations, leading to suboptimal predictions and forecasts.
Innovation Solution
A champion-challenger mechanism is implemented within an information handling system that automatically selects a new data analytics model by triggering inter-family and intra-family challenges based on correlation and fluctuation thresholds, allowing for continuous model evaluation and replacement to ensure accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static data analytics model is used, then the system is simple to operate, but the prediction accuracy deteriorates when data patterns change
Solution Approach 1:
The patent implements a dynamic model selection mechanism where the system automatically transitions between different analytics models (e.g., from linear regression to neural networks) based on real-time data pattern recognition. This allows the system to adapt its complexity level dynamically - using simple models when data patterns are stable and complex models when patterns change, thereby maintaining high prediction accuracy without permanent system complexity
Solution Approach 2:
The system employs self-service through automated champion-challenger mechanism that performs model evaluation, comparison, and selection without human intervention. The automated system monitors data patterns, triggers model challenges when changes are detected, and automatically selects the most appropriate model, eliminating the need for manual model management while maintaining high prediction accuracy
2Adaptability or versatility
If manual model selection is used, then the system complexity is low, but the responsiveness to data changes deteriorates
Solution Approach 1:
The patent implements continuous feedback loops where the system monitors data patterns, compares actual predictions against expected outcomes, and automatically triggers model re-evaluation when changes are detected. This feedback mechanism enables rapid adaptation to data changes by automatically initiating challenger model training and selection processes, achieving high responsiveness through targeted automation rather than complete system automation
Solution Approach 2:
The system performs preliminary actions by pre-training challenger models and maintaining a repository of candidate models ready for deployment. When data pattern changes are detected, the system can immediately switch to pre-prepared challenger models or quickly activate training processes, rather than starting from scratch. This preliminary preparation enables rapid response to data changes while keeping automation levels moderate
3Reliability
If frequent model changes are made, then the prediction accuracy improves, but the computational resources increase
Solution Approach 1:
The patent applies partial action by implementing selective model retraining only when data pattern changes are detected through statistical thresholds. Instead of continuously retraining all models or changing models frequently, the system performs model challenges only when necessary - when correlation changes or fluctuation thresholds are exceeded. This partial retraining approach maintains high prediction accuracy by updating models only when needed, while significantly reducing computational resource consumption compared to frequent continuous retraining
Data Source
AI summary
A method for forecasting includes obtaining input data from a data store, using a processor to forecast future data with a currently selected model, detecting a trigger event using a processor, training alternative models in a model family or from multiple families on the input data based on detecting in response to detecting the trigger event, identifying a replacement model from the alternative models using a processor, and using a processor to forecast future data with the replacement model.