SHAP-Based Interpretability Module for Time-Series Forecasting
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Solution Overview
Problem
Current machine learning models, particularly 'black box' models, lack interpretability for time-series forecasts, making it difficult for users to understand how training data relates to prediction results.
Innovation Solution
The method employs SHapley Additive exPlanations (SHAP) to compute feature importances and similarities between training data and predictions, using a heuristic function that combines SHAP and feature distances to visualize the importance of features in explaining time-series forecasts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a black box machine learning model is used for time-series forecasting, then prediction accuracy is improved, but interpretability of the model deteriorates
Solution Approach 1:
The patent introduces an interpretability module as an intermediary component that sits between the black box machine learning model and the user. This module computes similarity measures between training data points and forecasted points, generating explanations without modifying the core prediction model. The intermediary translates the opaque model outputs into interpretable visualizations showing which training examples most influenced each forecast, thereby recovering interpretability information without sacrificing prediction accuracy.
2Loss of information
If traditional chart-based interpretation methods are used, then interpretability is improved, but applicability to time-series data deteriorates
Solution Approach 1:
The patent creates a universal interpretability framework that adapts to different data types including time-series, tabular, images, and text. The core similarity computation mechanism is designed to be data-type agnostic, taking any training data and forecast as input and computing meaningful similarity measures. For time-series specifically, the system leverages the temporal structure by comparing sequences and identifying analogous historical patterns, making the interpretation method versatile across multiple domains while maintaining specialized effectiveness for time-series forecasting.
Data Source
AI summary
Methods and systems that provide machine learning interpretability. SHAP values of historical and predicted data, along with features of both, are used to provide a measure of the impact of training data points on a predictions. Removal of an individual training data point from a training data set, followed by comparing the resulting prediction with that obtained by the full training data set, also provides a measure of influence of individual training data points on forecasts.