Forecasting Model Parameter Validation Engine
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Solution Overview
Problem
Current forecasting software lacks the ability to effectively validate user selections for forecasting model parameters, leading to operational problems such as errors or low accuracy, as it fails to detect conflicts between parameter values that could cause issues with the selected model.
Innovation Solution
The system provides a graphical user interface that assists users in creating custom forecasting models by filtering options and validating parameter selections based on statistical information from the time series data, using a set of conflict rules to prevent the generation of problematic models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system provides extensive customization options for forecasting model parameters, then the adaptability and versatility of the forecasting software is improved, but the device complexity and difficulty of operation increase due to the large number of parameters users must configure
Solution Approach 1:
The patent introduces an automated validation engine as an intermediary between the user and the forecasting model parameters. This validation engine automatically checks parameter selections for conflicts and operational problems, acting as a mediator that guides users through the complex parameter space without requiring them to understand the underlying complexities. The system provides candidate parameter values and validates selections, reducing the cognitive burden on users while maintaining extensive customization capabilities.
2Ease of operation
If the system allows users to freely select any parameter values, then the ease of operation is improved, but the reliability decreases due to conflicts between parameter values that cause operational problems
Solution Approach 1:
The patent implements preliminary validation of parameter selections before the forecasting model is executed. The system provides candidate parameter values based on statistical information from the time series data and validates user selections against a set of conflict rules that identify operational problems. This preliminary action prevents unreliable model configurations from being created in the first place, maintaining both user freedom and model reliability.
Solution Approach 2:
The system provides immediate feedback to users when their parameter selections create conflicts or operational problems. The validation engine analyzes selected parameters and communicates issues back to the user, allowing them to adjust their selections. This feedback loop maintains ease of operation by keeping the user in control while ensuring reliability through continuous validation of parameter combinations.
3Reliability
If the system validates parameter selections to prevent operational problems, then the reliability is improved, but the productivity decreases due to additional validation steps in the model creation process
Solution Approach 1:
The validation process occurs in advance during the model creation phase, identifying and preventing conflicts before they can cause operational problems. By performing validation preliminarily, the system avoids the need for time-consuming debugging and troubleshooting later, ultimately improving productivity despite the additional validation steps.
Solution Approach 2:
The system provides automated validation that serves itself by automatically detecting and reporting parameter conflicts without requiring manual checking. The validation engine independently analyzes parameter selections and provides guidance, reducing the time users would otherwise spend manually verifying parameter compatibility while maintaining high reliability standards.
Data Source
AI summary
Some examples can involve a system that can receive a first user selection of time series data and a second user selection of a type of forecasting model to apply to the time series data. The system can then obtain a first set of candidate values and a second set of candidate values for a first parameter and a second parameter, respectively, of the selected type of forecasting model. The candidate values may be determined based on statistical information derived from the time series data. The system can then provide the first set of candidate values and the second set of candidate values to the user, receive user selections of a first parameter value and a second parameter value, and determine whether a conflict exists between the first parameter value and the second parameter value. If so, the system can generate an output indicating that the conflict exists.