Black Box Time-Series Model Evaluation via Feature Masking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex time-series models, particularly 'black-box' models, face challenges in explaining the impact of individual features on predictions over time due to their complexity and temporal dynamics, making it difficult to trust, understand, or validate their outputs, especially in critical domains like healthcare and finance.
Innovation Solution
The system determines importance scores by masking features in a time window and evaluating predictions with and without masking, using KL-divergence to assess the effect of features on model outputs, allowing for the evaluation of feature importance at specific times and across different timeframes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex black-box models are used to improve prediction accuracy for time-series data, then the model's predictive capability is improved, but the interpretability and understanding of feature impact on predictions deteriorates
Solution Approach 1:
The patent introduces an intermediary explanation system that mediates between the complex black-box model and human users. This system generates natural language explanations that translate complex model predictions into understandable feature-impact statements, allowing users to comprehend model behavior without simplifying the underlying complex model architecture.
Solution Approach 2:
The patent replaces traditional mechanical explanation methods (such as LIME or SHAP visualizations) with a natural language generation system. Instead of presenting users with complex graphical interfaces or mathematical metrics, the system substitutes these with human-readable text explanations that directly communicate feature impacts in contextualized narratives.
2Device complexity
If features are analyzed independently to simplify interpretation, then the analysis complexity is reduced, but the temporal relationships and dependencies between features are lost
Solution Approach 1:
The patent applies dynamics by treating feature analysis as a temporal process rather than a static snapshot. The system generates explanations that evolve over time, showing how feature impacts change across different time points in the time-series data. This dynamic approach preserves temporal relationships while maintaining interpretability through sequential natural language descriptions.
Solution Approach 2:
The system performs preliminary analysis of temporal relationships between features before generating explanations. By pre-processing the time-series data to identify and encode temporal dependencies, the system prepares this information in advance, allowing the explanation generation process to incorporate temporal context without adding complexity to the main interpretation task.
3Adaptability or versatility
If model-agnostic explanation methods are used to validate predictions, then the applicability to different models is improved, but the precision of feature importance measurement deteriorates
Solution Approach 1:
The patent creates a universal explanation framework that can be applied to any black-box model predicting time-series data. The natural language generation system is designed to work with any model output without requiring model-specific parameters or architecture knowledge, achieving broad adaptability while maintaining precise measurement of feature impacts through its specialized temporal analysis methods.
Data Source
AI summary
A model evaluation system evaluates the effect of a feature value at a particular time in a time-series data record on predictions made by a time-series model. The time-series model may make predictions with black-box parameters that can impede explainability of the relationship between predictions for a data record and the values of the data record. To determine the relative importance of a feature occurring at a time and evaluated at an evaluation time, the model predictions are determined on the unmasked data record at the evaluation time and on the data record with feature values masked within a window between the time and the evaluation time, permitting comparison of the evaluation with the features and without the features. In addition, the contribution at the initial time in the window may be determined by comparing the score with another score determined by masking the values except for the initial time.