Time Series Forecast Explainer for Black-Box Model Interpretability
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Solution Overview
Problem
Black box models in time series forecasting lack explainability, inhibiting clear understanding of prediction mechanisms and the impact of features on predictions, with existing explainers failing to provide comprehensive explanations for prediction intervals and time series data.
Innovation Solution
Development of interpretable, feature-based post-hoc black box explainers that generate local, semi-local, and global explanations using surrogate models and Shapley Additive exPlanations (SHAP) values, incorporating tree-based regressors and block bootstraps to explain forecasts of univariate time series.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If black box models are used for time series forecasting, then forecasting accuracy is improved, but explainability deteriorates
Solution Approach 1:
The patent introduces an intermediary explanation layer that mediates between the black box model's predictions and human understanding. This layer includes: (1) Feature importance analysis that identifies which input features most influenced predictions, (2) Prediction interval explanations that show uncertainty ranges and their causes, and (3) Counterfactual explanations that demonstrate how changing inputs would alter outputs. This intermediary structure preserves the high accuracy of black box models while providing comprehensive explanatory information.
2Loss of information
If comprehensive explanations are provided for prediction intervals and time series data, then interpretability is improved, but computational complexity increases
Solution Approach 1:
The patent segments the explanation generation process into distinct modular components: (1) Local explanation module for individual predictions, (2) Semi-local explanation module for time intervals, and (3) Global explanation module for overall model behavior. Each module handles specific aspects of interpretation independently, allowing parallel computation and reducing overall computational complexity while providing comprehensive interpretability across all levels.
Data Source
AI summary
A method, system, and computer program product for an interpretable, feature-based post-hoc black box explainer for univariate time series forecasters are provided. The method receives a set of time series forecasting predictions. The set of time series forecasting predictions are generated from a set of black-box models trained with an initial data set. The method generates a set of features based on at least a portion of the initial data set. A set of surrogate models are trained based on the set of time series forecasting predictions and at least a portion of the set of features. A subset of surrogate models is selected. Based on the subset of surrogate models, the method generates one or more explanation outputs for time series forecasting predictions of the set of black-box models.