Goal Seek Analysis Influential Predictor Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods lack an efficient automated approach to identify the most influential effects that contribute to a status change from normal to abnormal in goal seeking analysis, such as in monitoring indices like weight, blood pressure, or air pollution, making it difficult to adjust these effects to return the index to a normal status.
Innovation Solution
A computer-implemented method that collects data across time periods, generates a candidate list of significant changed predictors by comparing value distributions and series traits between normal and abnormal statuses, builds regression models, computes predictor importance values, and combines these values to determine the predictors that contribute most to the status change, allowing for the identification of key factors to adjust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis methods are used to identify influential predictors, then analysis accuracy may be maintained, but analysis efficiency and productivity are significantly reduced
Solution Approach 1:
The system performs automated self-analysis by collecting data, generating candidate lists of changed predictors, building regression models, and computing predictor importance values without requiring manual intervention. The algorithm independently identifies influential predictors through systematic computational processes, eliminating the need for manual analysis while maintaining scientific rigor through multiple validation steps.
Solution Approach 2:
The patent replaces manual analytical methods with an automated computational system that uses data collection, regression modeling, and algorithmic computation to identify influential predictors. The mechanical process of manual analysis is substituted with an automated information processing system that systematically evaluates predictors through standardized statistical methods.
2Measurement precision
If comprehensive data analysis is performed to ensure accurate identification of all influential predictors, then identification accuracy is improved, but computational complexity and time consumption increase
Solution Approach 1:
The analysis process is divided into distinct segments: data collection, candidate list generation through nonparametric testing, regression model building, predictor importance computation, and final ranking. Each segment handles a specific aspect of the analysis, breaking down the complex task into manageable steps that can be processed systematically and efficiently.
Solution Approach 2:
The system performs preliminary data collection and candidate list generation before building regression models. By pre-identifying significant changed predictors through nonparametric tests and series trait comparisons, the system reduces the scope of subsequent analysis, focusing computational resources on evaluating only the most promising candidates rather than analyzing all possible predictors.
3Reliability
If multiple regression models are built to ensure robust predictor identification, then reliability of results is improved, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary filtering through nonparametric tests and series trait comparisons to generate a candidate list of significant changed predictors before building regression models. This preliminary action reduces the number of predictors that require extensive modeling, allowing multiple regression models to be built more efficiently on a focused subset of candidates.
Solution Approach 2:
The system builds multiple regression models with overlapping time periods, using more models than strictly necessary to ensure robustness. This excessive action provides redundant validation, where multiple models converge on the same influential predictors, increasing confidence in the results. The computational cost is justified by the enhanced reliability and reduced need for repeated analysis.
Data Source
AI summary
A method for identifying influential effects that contribute most to a status change of a target index for goal seeking analysis. The method includes generating a candidate list of significant changed predictors between the normal and abnormal status time periods in collected data, and building a plurality of regression models from the collected data. The method determines a first value (trend value or Pearson correlation value) for each of the significant changed predictors based on whether at least one of the significant changed predictors have a significant change trend using the regression models. The method obtains a second predictor importance value for each of the significant changed predictors from a single model built on all the collected data. The method generates a final predictor value for each of the significant changed predictors by combining the first value with the second predictor importance value for each of the significant changed predictors.