Sequence Prediction Strategy Combiner
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Solution Overview
Problem
Current data prediction methods are deficient in accuracy and overall loss, failing to effectively predict next values in sequences with high confidence and low error rates across various industries.
Innovation Solution
A computing device combines predictions from multiple data prediction strategies using a weighted sum that favors more recent values, generating a single prediction with improved accuracy and lower loss by calculating deviations based on associated times and applying linear combinations through a strategy combiner, potentially utilizing a binary tree structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single data prediction strategy is used, then the system complexity is low, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The patent combines multiple independent data prediction strategies into a unified prediction system. Each strategy processes the sequence independently and produces a prediction with an associated confidence value. These predictions are then merged through a combination function that weights them according to their confidence values, creating a final prediction that leverages the strengths of multiple approaches while maintaining manageable complexity through modular architecture.
2Measurement precision
If all values in the sequence are weighted equally, then the calculation is simple, but the prediction accuracy decreases because recent trends are not captured
Solution Approach 1:
The patent applies local quality by assigning different weights to different positions in the sequence based on their temporal proximity. More recent values receive higher weights while older values receive lower weights, creating a non-uniform weighting scheme. This allows the prediction to be more sensitive to recent trends and patterns while still incorporating historical context, improving accuracy without requiring complex computational structures.
Data Source
AI summary
Multiple data prediction strategies are received. Each data prediction strategy may predict a next data value in a sequence of data values with a corresponding confidence value. Rather than rely on a single prediction strategy, the predictions of each of the data prediction strategies are linearly combined to generate a single prediction that is more accurate and has a lower overall loss than any of the individual prediction strategies. Further, a deviation is calculated based on the values in the sequence of values that have been observed so far using a weighted sum that favors more recent values in the sequence over less recent values in the sequence. A prediction of the next value in the sequence is generated based on the combined strategies and the calculated deviation.