Forecast Revision via Aggregation Circuitry
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Solution Overview
Problem
Machine learning systems face challenges in improving the accuracy and confidence of their predictions over time, as existing methods fail to effectively incorporate new measurements and adapt forecasts based on actual outcomes.
Innovation Solution
A data processing apparatus and method that includes forecast, measurement, and aggregation circuitry to generate and revise forecasts for future times by aggregating new measurements with previous forecasts, using techniques such as weighted averages and confidence interval adjustments, allowing for continuous tuning and refinement of predictions based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning systems use static forecast models, then the system complexity is low, but the prediction accuracy deteriorates over time as new data becomes available
Solution Approach 1:
The patent implements dynamic forecast models that automatically update their parameters and structure based on incoming measurements. The system transitions from static predictions to dynamic adaptive forecasting, where the model evolves continuously to maintain accuracy as new data arrives, directly resolving the contradiction between maintaining low complexity and achieving high prediction accuracy over time.
Solution Approach 2:
The system incorporates feedback loops where actual measurements are compared with forecasted values, and the differences (errors) are used to adjust and refine the forecast model. This continuous feedback mechanism enables the system to learn from past predictions and improve future accuracy without requiring complete model redesign, thus managing complexity while enhancing precision.
2Manufacturing precision
If machine learning systems incorporate new measurements continuously, then the prediction accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The patent applies partial updates to the forecast model, processing only the necessary portions of new data rather than retraining the entire model. This selective updating approach incorporates new measurements to improve accuracy while avoiding the excessive computational cost of complete model retraining, thus resolving the contradiction between accuracy improvement and resource consumption.
Solution Approach 2:
The system changes specific parameters of the forecast model based on new measurements rather than transforming the entire model structure. By adjusting only the relevant parameters that affect prediction accuracy, the system achieves continuous improvement while maintaining efficient computational resource usage and avoiding unnecessary processing overhead.
3Reliability
If machine learning systems revise forecasts based on aggregated data, then the confidence in predictions improves, but the processing time for each new measurement increases
Solution Approach 1:
The patent implements aggregation of historical forecast errors and measurements in advance, building cumulative statistical profiles before they are needed for revision. This preliminary aggregation allows the system to quickly revise forecasts using pre-computed statistics rather than processing all historical data each time, thus improving confidence while reducing real-time processing time.
Solution Approach 2:
The system applies different aggregation strategies and revision methods to different portions of the data based on their relevance and reliability. By selectively aggregating only the most informative local data segments rather than uniformly processing all data, the system enhances prediction confidence while minimizing unnecessary processing time and computational overhead.
Data Source
AI summary
A data processing apparatus is provided that includes forecast circuitry for generating a forecast of an aspect of a system for a next future time and for one or more subsequent future times following the next future time. Measurement circuitry generates, at the next future time, a new measurement of the aspect of the system. Aggregation circuitry produces an aggregation of the forecast of the aspect of the system for the next future time and of the new measurement of the aspect of the system. The forecast circuitry revises the forecast of the aspect of the system for the one or more subsequent future times using the aggregation.