Dynamic Model Ensemble Selection for Prediction Stability
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Solution Overview
Problem
Existing machine learning approaches for predicting future values over time often lead to over-fitting and inefficiencies due to a lack of consideration for changing environments and realities, as they primarily focus on error-based metrics without accounting for non-error factors such as model switches and sign changes.
Innovation Solution
A method that generates a model ensemble by selecting models based on prediction accuracy and non-error metrics like the number of unique models, model switches, and sign changes, allowing for dynamic adaptation to changing conditions and reducing over-fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems utilize the most-accurate model at each interval by focusing solely on error-based metrics, then prediction accuracy at each stage is improved, but over-fitting occurs and the system fails to account for changing environments
Solution Approach 1:
The patent changes the selection criteria parameters from solely error-based metrics to a combination of error metrics and non-error metrics (such as model complexity, number of parameters, or computational cost). This allows the system to select models that balance accuracy with stability, preventing over-fitting while adapting to changing environments over multiple intervals
Solution Approach 2:
The patent implements dynamic model selection across multiple time intervals, where the selected model can change based on current performance and non-error metrics. This dynamic approach allows the system to adapt to changing environmental conditions while maintaining reliability through constraints on model switching frequency and selection criteria
2Ease of manufacture
If models are trained using only prior data without considering new ongoing data, then training completeness is improved, but the models fail to account for changing environments and realities
Solution Approach 1:
The patent implements feedback mechanisms where model predictions are continuously evaluated against actual outcomes from ongoing projects. This feedback loop allows the system to identify when current models are becoming inaccurate due to environmental changes, triggering retraining or model selection adjustments while maintaining training completeness
Solution Approach 2:
The patent performs preliminary model selection and evaluation across multiple intervals before final deployment, assessing both error metrics and non-error metrics. This preliminary action ensures models are thoroughly vetted for both accuracy and adaptability to changing conditions before being applied to ongoing projects
3Measurement precision
If the system switches between models frequently to maintain accuracy at each interval, then prediction accuracy is improved, but resource usage increases and over-fitting occurs
Solution Approach 1:
The patent changes the model selection parameters to include non-error metrics such as model complexity, computational cost, and switching frequency constraints. This allows the system to select models that maintain accuracy while reducing resource usage by avoiding unnecessary model switches and selecting computationally efficient models
Solution Approach 2:
The patent applies partial model switching rather than frequent complete switches, using ensemble methods or hybrid approaches where multiple models contribute to predictions. This reduces the frequency and computational cost of model switching while maintaining prediction accuracy through coordinated model usage
Data Source
AI summary
Techniques for generating model ensembles are provided. A plurality of models trained to generate predictions at each of a plurality of intervals is received. A respective prediction accuracy of each respective model of the plurality of models is determined for a first interval of the plurality of intervals by processing labeled evaluation data using the respective model. Additionally, a model ensemble specifying one or more of the plurality of models for each of the plurality of intervals is generated, comprising selecting, for the first interval, a first model of the plurality of models based on (i) the respective prediction accuracies and (ii) at least one non-error metric.