Model Quality Index for Storage Capacity Planning
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Solution Overview
Problem
Conventional statistical modeling and forecasting approaches lack a reliable, objective, and easy-to-understand method for evaluating model quality, leading to potential biases and suboptimal decisions due to reliance on single parameters and requiring skilled users to interpret complex data outputs.
Innovation Solution
The integration of a Model Quality Index (MQI) system that combines multiple parameters into a single, objective indicator, allowing for automatic evaluation of model quality and adequacy, using engines for data analysis, modeling, and forecasting to generate a forecast model and evaluate its quality based on combined parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple model quality parameters are used to evaluate model quality, then the evaluation becomes more comprehensive and reliable, but the complexity of interpretation increases and requires skilled users
Solution Approach 1:
The patent combines multiple model quality parameters (R-squared, AIC, BIC, residual analysis, etc.) into a single integrated Model Quality Index (MQI) score. This merging approach maintains comprehensive evaluation while simplifying interpretation, as the MQI provides a unified metric that reflects overall model quality without requiring users to separately analyze multiple individual parameters.
Solution Approach 2:
The Model Quality Index acts as an intermediary between the complex set of model parameters and the user's decision-making process. Instead of directly presenting multiple technical parameters that require expert interpretation, the MQI translates them into a single intermediate metric that is easier to understand and use for model selection and evaluation.
2Ease of operation
If a single parameter is used to evaluate model quality, then the evaluation process is simplified and easier to automate, but the evaluation becomes biased and may miss important model deficiencies
Solution Approach 1:
The patent merges multiple individual parameter evaluations into a single comprehensive MQI score. This allows the system to maintain the simplicity of single-parameter evaluation (easy to automate and interpret) while incorporating the reliability benefits of multi-parameter assessment by combining R-squared, AIC, BIC, residual analysis, and other metrics into one unified index.
3Reliability
If manual interpretation of model parameters is performed, then skilled analysts can make informed decisions, but the process is time-consuming and not usable by non-experts
Solution Approach 1:
The Model Quality Index enables the evaluation system to serve itself by automatically computing and interpreting model quality without requiring external expert intervention. The MQI formula and calculation methodology are self-contained, allowing the system to autonomously evaluate models and provide actionable insights, eliminating the need for time-consuming manual analysis by skilled analysts.
Solution Approach 2:
The MQI serves as an intermediary that bridges the gap between complex model parameters and non-expert users. By translating multiple technical parameters into a single intuitive score, it enables anyone to make informed decisions about model quality without requiring specialized statistical knowledge or time-consuming manual analysis.
4Adaptability or versatility
If conventional model evaluation criteria are used, then existing tools can be utilized, but the criteria may be misleading and drive decision-makers in the wrong direction
Solution Approach 1:
The patent combines multiple conventional evaluation criteria (R-squared, AIC, BIC, residual analysis) into a single MQI that provides a more reliable overall assessment. This merging allows the system to utilize existing tools and metrics while avoiding their individual limitations, as the combined MQI prevents misleading conclusions that might arise from relying on any single criterion.
Data Source
AI summary
A robust, simple, application-specific way to evaluate data models and forecasts is provided for evaluating whether a forecast is trustworthy. An approach for formulating a single, summary indication of data/model/forecast quality relevant for the task at hand is described. This approach includes generating a forecast model from collected data, combining multiple model-quality parameters based on the model, computing an indication based on the combination of parameters, and evaluating the model and forecast quality based on the indication. This indication, in the form of a Model Quality Index, can also be used to compare different types of models produced by different types of analysis approaches.