Surrogate Model for Time-Series Interpretability
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Solution Overview
Problem
Training time-series forecasting models is laborious due to the difficulty in extracting explanatory insights on model characteristics, leading to a trade-off between model complexity and predictive accuracy, where simpler models are chosen for interpretability but result in lower accuracy.
Innovation Solution
A host system that uses a surrogate model to enhance model selection by determining interpretability scores, generating a composite model that includes both a core model for accuracy and a surrogate model for interpretability, allowing for simultaneous debriefing during training and improving model interpretability without compromising accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex time-series forecasting model is trained to improve predictive accuracy, then the model's predictive accuracy is improved, but the model complexity increases making it difficult to extract explanatory insights
Solution Approach 1:
The patent introduces a surrogate model as an intermediary between the complex core model and the user. The surrogate model is trained to replicate the core model's predictions but with simplified structure that enables interpretability. This intermediary allows users to gain explanatory insights without directly analyzing the complex core model, thus resolving the contradiction between accuracy and interpretability.
Solution Approach 2:
The patent creates a simplified copy (surrogate model) of the complex core model. The surrogate model copies the predictive behavior of the core model but uses a simpler structure that is easier to interpret. This copying approach allows the system to maintain high predictive accuracy through the core model while providing interpretability through the simplified surrogate model.
2Ease of operation
If a simple time-series forecasting model is chosen for ease of interpretation, then the ease of operation is improved, but the predictive accuracy deteriorates
Solution Approach 1:
The patent segments the modeling function into two separate components: a core model that handles predictive accuracy and a surrogate model that handles interpretability. This segmentation allows each component to be optimized for its specific purpose without compromise - the core model can be complex for accuracy while the surrogate model remains simple for interpretability, thus resolving the contradiction.
Solution Approach 2:
The patent creates a multi-functional system where the core model provides predictive accuracy and the surrogate model provides interpretability. Together, they fulfill both requirements that a single model would struggle to meet. The system as a whole achieves universality by combining the strengths of different model types to serve multiple purposes simultaneously.
3Measurement precision
If greater procedure complexity is applied to create a more accurate model, then the predictive accuracy is improved, but the difficulty of detecting and measuring model characteristics increases
Solution Approach 1:
The surrogate model serves as an intermediary that translates the complex internal characteristics of the core model into easily interpretable forms. It detects and measures model characteristics in a simplified manner, making explanatory insights accessible without requiring direct analysis of the complex core model, thus resolving the contradiction between accuracy and measurability.
Solution Approach 2:
The surrogate model creates a simplified copy of the core model's behavior that preserves predictive accuracy while making characteristics easier to detect and measure. By copying the essential predictive patterns in a simplified structure, the system enables easy extraction of explanatory insights without sacrificing accuracy.
Data Source
AI summary
Provided is a system and method which build a composite time-series machine learning model including a core model and a debrief model that includes a combination of the core model and a surrogate model. In one example, the method may include executing the plurality of models on test data and determining accuracy values and interpretability toughness values for the plurality models, selecting a most accurate model as a core model based on the accuracy values and select a most interpretable model as a surrogate model from among other models remaining in the plurality of models based on the interpretability toughness values, building a composite model comprising the core model, the surrogate model, and instructions for generating a debrief model for debriefing the core model based on a combination of the core model and the surrogate model, and storing the composite model within the memory.