Forecasting Model Parameter Calibration for Unbiased Predictions
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Solution Overview
Problem
Current forecasting models are inefficient and resource-intensive, particularly when dealing with large numbers of competing products, as they rely on syndicated data and are often manually created, leading to biased predictions and wastage of computing resources.
Innovation Solution
A forecasting model platform that selects optimum primary and secondary parameters to calibrate and generate unbiased forecasting models by receiving demand data and multiple forecasting models, ranking them based on cost functions, and selecting the best parameters to conserve resources and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual forecasting models are created using syndicated data, then the models can be generated with existing data sources, but the models become biased and resource-intensive
Solution Approach 1:
The patent replaces manual forecasting model creation with an automated machine learning system that uses algorithms to select and calibrate models, eliminating the need for manual intervention and reducing computing resource waste while improving accuracy through systematic evaluation of multiple models
Solution Approach 2:
The system automatically adjusts and optimizes model parameters through calibration processes, transforming fixed manual parameters into dynamically optimized values that improve forecasting accuracy while reducing the computational burden of trial-and-error manual tuning
2Reliability
If multiple forecasting models are evaluated manually, then the best model can be selected, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary automated evaluation of multiple forecasting models against calibration datasets before final deployment, pre-identifying the most promising models and their optimal parameters, which reduces the time and resources needed for final model selection and calibration
Solution Approach 2:
The forecasting system automatically evaluates, compares, and selects the best models without manual intervention, using self-contained algorithms to assess model performance and make selections, thereby eliminating time-consuming manual review processes while maintaining or improving selection quality
3Ease of manufacture
If syndicated data is used for forecasting, then data collection is simplified, but the forecasts become biased and less accurate
Solution Approach 1:
The system segments the data collection process by using syndicated data only for specific calibration purposes while combining it with other data sources for comprehensive model evaluation, allowing the benefits of simplified data collection to be maintained while mitigating bias through diversified data inputs
Solution Approach 2:
The patent introduces machine learning algorithms as intermediaries that process and adjust syndicated data, transforming potentially biased raw data into calibrated inputs that maintain the ease of data collection while improving forecasting accuracy through systematic adjustment and validation
Data Source
AI summary
A device may receive demand data associated with a product or a service, multiple forecasting models, and multiple cost functions, and may identify primary parameters for the multiple models based on the demand data. The device may utilize a model to rank the multiple forecasting models and the multiple cost functions based on the primary parameters, and may select optimum primary parameters based on ranking the multiple forecasting models and the multiple cost functions. The device may identify secondary parameters for the multiple forecasting models based on the demand data and the optimum primary parameters. The device may select optimum secondary parameters based on ranking the multiple forecasting models and the multiple cost functions, and may select a forecasting model, from the multiple forecasting models, based on the optimum primary parameters and the optimum secondary parameters. The device may perform one or more actions based on selecting the forecasting model.