Forecasting Model Complexity Selection via Eigenvalue Analysis
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Solution Overview
Problem
Existing forecasting models face challenges in accurately predicting outcomes due to mismatched model complexity, leading to either erroneous predictions or high computational costs, as they fail to adapt to changes in the relationship between factors and outcomes over time.
Innovation Solution
The method selects forecasting model complexity using eigenvalues, determining differences between predicted and actual outcomes, and adjusting model complexity based on eigenvalue changes and thresholds to maintain accurate predictions while minimizing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex forecasting model is used to improve prediction accuracy, then prediction accuracy is improved, but computational cost increases
Solution Approach 1:
The patent implements dynamic model complexity adjustment by monitoring eigenvalue changes in the data. The system transitions from a static model selection approach to a dynamic one where the model complexity adapts over time based on the observed data characteristics, specifically using eigenvalue distribution changes to trigger model reselection and maintain optimal prediction accuracy while minimizing computational costs.
Solution Approach 2:
The patent changes the parameter of model complexity based on eigenvalue analysis. By computing eigenvalues from the data covariance matrix and monitoring their distribution changes, the system selectively adjusts the complexity parameter of the forecasting model, switching between simple and complex models according to the actual data characteristics rather than using a fixed complexity level.
2Use of energy by moving object
If a simple forecasting model is used to reduce computational cost, then computational cost is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The system dynamically selects between simple and complex models based on real-time eigenvalue analysis of the data. Rather than always using a simple model, the system monitors changes in eigenvalue distributions and automatically increases model complexity when the data characteristics indicate it is needed, thus maintaining prediction accuracy while minimizing computational costs during periods when simple models suffice.
Solution Approach 2:
The patent adjusts the model complexity parameter based on eigenvalue distribution changes. By computing eigenvalues and analyzing their distribution over time, the system dynamically modifies the complexity parameter of the forecasting model, selecting simpler models when data characteristics permit and more complex models when accuracy requirements demand, thereby optimizing the trade-off between computational cost and prediction accuracy.
3Adaptability or versatility
If model complexity is increased to adapt to changing relationships between factors and outcomes, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements a dynamic model selection mechanism that monitors eigenvalue changes to detect shifts in the relationships between factors and outcomes. The system adaptively adjusts model complexity in response to these changes, transitioning from static to dynamic model configuration. This allows the system to maintain high adaptability by using complex models only when necessary, rather than maintaining permanently high model complexity.
Solution Approach 2:
The system changes the model complexity parameter based on eigenvalue analysis of the data characteristics. By computing eigenvalues from the data covariance matrix and monitoring their distribution, the patent dynamically adjusts the complexity parameter of the forecasting model, increasing it only when the data characteristics indicate a need for greater adaptability, thus avoiding unnecessary model complexity while maintaining the ability to adapt to changing relationships.
4Measurement precision
If eigenvalue analysis is used to dynamically adjust model complexity, then prediction accuracy is maintained, but computational overhead increases
Solution Approach 1:
The patent applies partial eigenvalue analysis rather than complete reanalysis at each time step. The system monitors eigenvalue distribution changes and only triggers model reselection when significant changes are detected, rather than continuously adjusting the model. This partial action approach maintains prediction accuracy while reducing the computational overhead associated with constant eigenvalue computation and model retraining.
Solution Approach 2:
The system changes model complexity parameters based on eigenvalue analysis, but implements this change efficiently by monitoring eigenvalue distribution changes over time and only triggering model reselection when threshold-based significant changes occur. This parameter change approach maintains prediction accuracy by responding to actual data characteristic changes while minimizing unnecessary computational overhead from frequent model adjustments.
Data Source
AI summary
A method, system, and computer program product for selecting forecasting model complexity using eigenvalues are provided in the illustrative embodiments A process is represented in a model. The model comprises a mathematical representation of the process in a certain degree. A first portion of historical data generated by the process during a first period is selected and includes an actual value of an outcome of the process and a value of a feature influencing the process during the first period. A prediction is made of a predicted value of the outcome. A difference between the prediction and the actual value of the outcome is determined. The difference is represented as a change in a distribution of eigenvalues. According to the change, a second model is to represent the process. The second model comprises a second mathematical representation of the process in a different degree.