Forecasting Probability Curves for Variable State Evolution
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Solution Overview
Problem
Existing forecasting methods face challenges in accurately predicting the time evolution of multiple variables affecting decisions, as they often require holding variables constant, leading to limited accuracy and increased costs due to the need for multiple predictions and complex decision-making processes.
Innovation Solution
A computer-implemented method and system that determines and displays substantially continuous probability curves for multiple variables over overlapping intervals in real time, allowing users to compare relative probabilities and make informed decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple forecasts are performed and compared to improve decision accuracy, then the reliability of the decision improves, but the loss of time and productivity deteriorate due to the trial and error nature of predictions
Solution Approach 1:
The system performs preliminary forecasting by determining probability curves for multiple variables in advance, allowing decision-makers to see potential outcomes before making decisions. This eliminates the need for trial-and-error predictions during the decision-making process, as the forecasts are prepared beforehand and can be compared to inform the decision.
Solution Approach 2:
The system provides continuous probability curves that show the evolution of variable states over time intervals, rather than discrete point predictions. This continuous representation allows for more comprehensive comparison of multiple scenarios without requiring multiple separate prediction runs, maintaining the usefulness of the action across the entire time horizon.
2Reliability
If multiple forecasts are performed to improve decision accuracy, then the reliability of the decision improves, but the cost and complexity of the process worsen
Solution Approach 1:
The system merges multiple variable forecasts into a single integrated display showing probability curves for all variables within the domain. Instead of performing and comparing multiple separate forecast runs, the system combines the probability assessments of multiple variables into one unified visualization, reducing the complexity of managing multiple independent predictions.
Solution Approach 2:
The system provides a universal forecasting framework that handles multiple variables with different time intervals simultaneously. The probability curve determination mechanism works universally across all variables in the domain, allowing a single system to perform what would otherwise require multiple specialized prediction processes.
3Device complexity
If variables are held constant in forecasting to simplify the model, then the device complexity reduces, but the measurement precision and reliability of predictions worsen
Solution Approach 1:
The system dynamically models variables by allowing each variable to change state over time according to its own probability curve, rather than holding variables constant. The probability curves capture the dynamic evolution of each variable through overlapping time intervals, enabling the model to adapt to changing conditions without requiring complex manual adjustments.
Solution Approach 2:
The system changes the parameters of variables over time by determining probability curves that evolve through different time intervals. Instead of fixing variable parameters, the system allows parameters to change dynamically according to the probability distributions determined for each variable, improving prediction accuracy while maintaining manageable model complexity.
Data Source
AI summary
Devices and methods for use in forecasting the state of one or more of a plurality of variables within a domain are provided. One example method includes determining, at a processor, a first probability curve indicative of a probability of a change-in-state of the first variable over a first interval, the first probability defining a first substantially continuous time trajectory, determining, at the processor, a second probability curve indicative of a probability of a change-instate of the second variable over a second interval, the second probability defining a second substantially continuous time trajectory, the first interval at least partially overlapping with the second interval, and displaying, at a display device, the first and second probability curves substantially in real time, thereby permitting a user to compare the relative probabilities of the change-in-state of at least one of the first and second variables.