Self-Managed Adaptive Models for Prediction Systems
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Solution Overview
Problem
Current predictive modeling systems face challenges in managing complex phenomena that require multiple analytical models and scalable automatic management of derived models and analytical results, especially in large-scale prediction systems where a single model is insufficient to explain complex patterns.
Innovation Solution
The implementation of self-managed adaptable models that automatically couple multiple analytical models with data sources, allowing for dynamic binding of input data streams to optimal model classes, creation of new models, model validation, and maintenance of model versions, enabling the system to adapt to new data streams and predict target variables effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple analytical models are used to explain complex phenomena, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple analytical models into a unified predictive system that automatically manages and coordinates them. The system merges model management, data stream processing, and validation functions into an integrated framework that handles complex phenomena through coordinated model ensembles rather than isolated models.
Solution Approach 2:
The predictive system is designed with universal components that can handle multiple model types and data sources through standardized interfaces. The system provides multi-functional capabilities including automatic model selection, data stream binding, validation, and version control through a single unified platform that works across diverse analytical models.
2Productivity
If automatic management of derived models and analytical results is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements self-service automation where the predictive framework automatically performs model management tasks including binding data streams to models, validating model outputs, tracking model versions, and generating predictions without manual intervention. The system serves itself by autonomously managing its own complexity through automated workflows and self-contained validation mechanisms.
Solution Approach 2:
The system performs preliminary actions by pre-configuring model management frameworks, data stream bindings, and validation rules before actual prediction tasks. Models are pre-validated and registered in advance, and data stream relationships are established beforehand, enabling efficient automatic management during production without real-time complexity.
3Adaptability or versatility
If dynamic binding of input data streams to optimal model classes is performed, then adaptability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system uses feedback mechanisms to dynamically bind data streams to optimal model classes by continuously monitoring data characteristics and model performance. The framework detects data stream properties, compares them against model requirements, and automatically selects the best-matching models based on performance feedback and compatibility metrics, making the adaptability process measurable and controllable.
Solution Approach 2:
The system manages adaptability by changing parameters such as data stream characteristics, model performance metrics, and compatibility thresholds to optimize the binding process. By adjusting these parameters dynamically, the system can detect and measure the optimal model-data stream matches without excessive complexity, using parameter-based decision rules rather than complex analytical comparisons.
Data Source
AI summary
Embodiments for self-managed adaptable models for prediction systems by one or more processors. One or more adaptive models may be applied to data streams from a plurality of data sources according to one or more data recipes such that the one or more adaptive models predict a plurality of target variables.