Variable Attribution for Time-Series Models
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Solution Overview
Problem
Complex machine learning models, such as neural networks, are often 'black boxes' due to their high accuracy and complexity, making it difficult to interpret how changes in inputs affect outputs, which hinders their usability and validation.
Innovation Solution
The Variable Attribution for Time-Series (VATS) method generates all combinations of dynamic variable values between two instances, calculates differences in model predictions, and averages these differences to determine the attribution of a target variable's change, providing a clear attribution of changes in model predictions to dynamic variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models are used to predict complex systems, then prediction accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent segments the complex model analysis into two distinct parts: (1) using complex machine learning models for accurate predictions, and (2) using separate attribution methods to analyze input-output relationships. This segmentation allows both high accuracy and interpretability to coexist by decoupling the prediction function from the explanation function.
Solution Approach 2:
The patent introduces attribution methods as an intermediary layer between the complex model and the user. This intermediary translates the black-box model outputs into interpretable attributions that show which inputs contributed most to predictions, without requiring changes to the underlying complex model architecture.
2Adaptability or versatility
If complex machine learning models with many inputs and hidden layers are used, then prediction capability is improved, but ease of interpretation deteriorates
Solution Approach 1:
Attribution methods serve as an intermediary that bridges the gap between complex model internals and human interpretation. They translate hidden layer computations into meaningful attributions for each input feature, making complex model behavior understandable without simplifying the model itself.
Solution Approach 2:
The patent employs visualization techniques that use color coding to represent attribution magnitudes and directions. This visual encoding transforms abstract numerical attributions into intuitive visual patterns, significantly improving ease of interpretation for complex models with many inputs.
3Device complexity
If simpler models are used instead of complex machine learning models, then interpretability is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent segments the solution into two independent components: a complex model for accurate predictions and a separate attribution analysis system for interpretability. This allows retaining the complex model's prediction accuracy while providing simplified explanations through attribution methods.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex model's behavior through attribution scores. Instead of using the actual complex model for interpretation, it generates a simplified attribution profile that mirrors the model's input-output relationships in an easily interpretable format.
4Productivity
If machine learning models are used to predict complex systems, then useful predictions are achieved, but ability to determine why changes in inputs produce changes in outputs deteriorates
Solution Approach 1:
The patent implements feedback by using attribution information to inform and improve the modeling process. The attribution results provide feedback about which inputs are most influential, allowing users to adjust inputs strategically and validate model behavior, thereby recovering causal information that would otherwise be lost.
Solution Approach 2:
Attribution methods act as an intermediary that recovers and communicates causal information from the black-box model. They translate internal model computations into meaningful insights about input-output relationships, preventing loss of causal information while maintaining prediction accuracy.
Data Source
AI summary
A method of determining the effect that changes in input variables have on changes in the output of a time series model, between two instances of time, produces variable attributions that satisfy the Shapley fairness properties of efficiency, symmetry, linearity, and null player.