Forecast Model Calibration for Resource Allocation
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Solution Overview
Problem
Forecasting models often fail to provide accurate quantified predictions, leading to incorrect resource allocation and planning, as they do not effectively match forecast levels with actual levels, resulting in unnecessary deployments or under-preparedness.
Innovation Solution
A computer-implemented method and system for calibrating forecast models using historical data to adjust the mapping between quantified forecasts and forecast levels, ensuring accurate resource allocation by comparing forecast levels with actual levels based on specific metrics and adjusting the model to improve matching across intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If forecast models use historical data to generate quantified predictions, then resource allocation can be planned in advance, but the forecast levels do not accurately match actual levels leading to allocation errors
Solution Approach 1:
The patent applies parameter changes by adjusting the mapping between quantified forecast values and forecast levels through calibration. The system modifies the relationship parameters (mapping thresholds and level boundaries) based on historical performance data, transforming the forecast model's output characteristics to improve accuracy while maintaining the planning time benefit
Solution Approach 2:
The patent implements feedback by comparing forecast levels with actual levels using historical data, then using this comparison to adjust and recalibrate the forecast model. This closed-loop feedback mechanism continuously improves forecast accuracy by learning from past performance patterns and applying corrections to future predictions
2Productivity
If forecast models provide quantified predictions without calibration, then resource allocation decisions can be made quickly, but unnecessary deployments or under-preparedness occur
Solution Approach 1:
The patent applies preliminary action by performing calibration of the forecast model before actual resource allocation decisions are made. The system pre-adjusts the mapping between quantified forecasts and forecast levels using historical data, so that when allocation decisions are needed, the model is already optimized and ready to provide accurate predictions without delaying the decision-making process
Solution Approach 2:
The system uses feedback from historical comparisons between forecast and actual levels to continuously improve allocation reliability. By analyzing past performance and adjusting the forecast model accordingly, the system maintains high accuracy while preserving quick decision-making capabilities
Data Source
AI summary
A system and method perform calibration of a forecast model for resource allocation. The method includes receiving inputs to the forecast model derived from historical data for a period of time, and executing the forecast model to obtain one or more forecast levels for each interval within the period of time, the forecast level corresponding with a quantified forecast of a forecast parameter that is forecast by the forecast model for the interval. Obtaining an actual level for each interval within the period of time according to the historical data is followed by comparing the one or more forecast levels with the actual level for the period of time according to a metric to adjust a mapping within the forecast model between values of the quantified forecast and the forecast levels based on the comparing to obtain a calibrated forecast model. The calibrated forecast model is used for resource allocation.