Neural Network Probabilistic Forecasting with Reject Option
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Solution Overview
Problem
Existing machine learning models for probabilistic forecasting face challenges in handling high uncertainty, often resulting in inaccurate predictions when extremely uncertain about input data, which can lead to harmful downstream consequences, and conventional approaches fail to integrate a reject option during training.
Innovation Solution
A computer-implemented system and method for training a neural network that incorporates a selection/reject option during training, allowing the model to abstain from making predictions when highly uncertain, by thresholding the predictive distribution and updating weights based on a loss function that considers the reject option, thereby avoiding optimization resources for uncertain inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neural network makes predictions for all input data including highly uncertain cases, then the model covers all possible inputs, but the prediction accuracy deteriorates due to high uncertainty leading to harmful downstream consequences
Solution Approach 1:
The patent extracts highly uncertain predictions from the overall prediction process by introducing a selection value and threshold mechanism. When the selection value indicates high uncertainty (below threshold), the prediction is rejected and removed from the output, preventing harmful downstream consequences while maintaining reliability for certain predictions.
Solution Approach 2:
The patent makes the prediction system dynamic by allowing it to adaptively select whether to make a prediction based on uncertainty levels. The neural network dynamically adjusts between making predictions and rejecting them based on the selection value, enabling flexible response to varying input uncertainty without fixed coverage requirements.
2Reliability
If the neural network integrates a reject option during training, then the prediction reliability improves by avoiding uncertain predictions, but the model complexity increases due to additional selection value computation and threshold mechanisms
Solution Approach 1:
The patent merges the prediction generation and uncertainty assessment into a single integrated process. The neural network simultaneously outputs both the prediction and the selection value in one forward pass, eliminating the need for separate uncertainty evaluation modules and reducing overall system complexity despite adding the reject option capability.
Solution Approach 2:
The neural network is designed with multi-functionality to serve both prediction and uncertainty assessment purposes. The same network architecture and training process generate both the prediction output and the selection value, making the model universally capable of both tasks without requiring additional specialized components.
3Productivity
If the loss function considers the reject option during training, then the optimization efficiency improves by avoiding uncertain inputs, but the training complexity increases due to modified loss computation involving selection values
Solution Approach 1:
The patent applies preliminary action by computing the selection value and applying the modified loss function during the training process itself, rather than adding post-processing rejection steps. The loss function is designed to penalize incorrect predictions and reward correct rejections, preparing the model in advance to make reliable predictions without needing additional training phases.
Data Source
AI summary
A computer-implemented system and method for training a machine learning model are disclosed, the method includes: maintaining a data set representing a neural network having a plurality of weights; receiving input data comprising a plurality of time series data sets ending with timestamp t−1; generating, using the neural network and based on the input data, a probabilistic forecast distribution prediction at timestamp t and a selection value associated with the probabilistic forecast distribution prediction at timestamp t; computing a loss function based on the selection value; and updating at least one of the plurality of weights of the neural network based on the loss function.