Forecasting Model Blindspot Correction via Qualitative Intervention
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Solution Overview
Problem
Existing forecasting models often fail to accurately predict system behavior in certain ranges of input values, known as 'blindspots,' due to limited data or potential destructive testing scenarios.
Innovation Solution
The proposed solution involves a system that identifies qualitative information indicating system operation differs from the forecasting model's assumptions. This information is used to generate intervention data, which is applied to modify the forecasting model within specific input value ranges, particularly those with limited data or potential for destructive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a forecasting model is generated using traditional data-driven methods, then the model can be built with available data, but the model fails to accurately predict system behavior in certain input value ranges known as 'blindspots' due to limited data
Solution Approach 1:
The patent introduces qualitative information as an intermediary element that bridges the gap between available quantitative data and system behavior in blindspot regions. This qualitative information acts as a mediator that provides guidance on system behavior where quantitative data is insufficient, allowing the forecasting model to make accurate predictions in previously uncoversable input ranges without requiring extensive destructive testing to collect data
2Reliability
If testing is performed to gather more data for improving model accuracy, then the forecasting model can cover more input ranges, but testing may be destructive to the system
Solution Approach 1:
The patent applies preliminary action by incorporating qualitative information about system behavior into the forecasting model before actual testing or operation in blindspot regions. This allows the model to be pre-configured with knowledge of system behavior characteristics, enabling accurate predictions without requiring destructive testing to discover these behaviors. The qualitative information serves as advance knowledge that prevents the need for harmful exploratory testing
Solution Approach 2:
The patent converts the limitation of not having data in blindspot regions into a benefit by using qualitative information to infer system behavior in these regions. Instead of viewing limited data coverage as a harmful constraint, the system uses qualitative descriptions of system behavior to fill these gaps, transforming the data scarcity problem into an opportunity to integrate domain knowledge and improve model robustness without additional testing
3Measurement precision
If the forecasting model is modified to incorporate qualitative information, then the model sensitivity and accuracy are enhanced, but the model complexity increases
Solution Approach 1:
The patent applies local quality by modifying the forecasting model to incorporate qualitative information specifically in blindspot regions where it is most needed, rather than uniformly across all input ranges. This localized approach enhances prediction accuracy in critical areas while maintaining the simplicity of the original model structure in well-covered regions, thereby improving overall performance without proportionally increasing complexity across the entire system
Data Source
AI summary
Techniques regarding modifying a forecasting model are provided. For example, one or more embodiments described herein can comprise a modeling system, which can comprise a memory that can store computer executable components. The modeling system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include an identifying component that can identify qualitative information related to a forecasting model of an operation of a system, wherein the forecasting model was generated through data obtained from the operation of the system. The computer executable components can include a modifying component that can modify the forecasting model based on intervention information generated based on the qualitative information, wherein the intervention information is associated with a prediction of the forecasting model.