Adaptive Diffusion Model for Data Stream Parameter Estimation
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Solution Overview
Problem
Existing predictive models are unsuitable for real-time decision-making in high-velocity data streams with non-stationary dynamics, as they fail to adapt quickly to changes in underlying data stream dynamics, making it challenging to identify anomalies, detect regime shifts, and make accurate predictions.
Innovation Solution
A computer-implemented method using a diffusion model with adaptive parameter estimation via stochastic gradient descent, where parameter estimates are updated in real-time using a sampling distribution that prioritizes recent data points, allowing the model to dynamically adjust to changing data stream dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing predictive models are used for data stream analysis, then model simplicity is maintained, but the models fail to adapt quickly to changes in non-stationary data stream dynamics
Solution Approach 1:
The patent implements dynamic model adaptation by continuously updating diffusion parameters through stochastic gradient descent as new data points arrive. The model transitions from a static structure to a dynamic one that evolves with the data stream, allowing parameters to change over time while maintaining computational efficiency through online learning mechanisms.
Solution Approach 2:
The patent changes the diffusion parameters of the predictive model in response to incoming data, using stochastic gradient descent to update parameter estimates. This allows the model to adapt its parameters dynamically without requiring complete model retraining, thus achieving adaptability while controlling complexity through incremental parameter adjustments.
2Productivity
If real-time adaptive modeling is implemented using stochastic gradient descent, then adaptability to non-stationary dynamics is improved, but computational cost increases
Solution Approach 1:
The patent applies partial action by updating only the diffusion parameters of the model rather than retraining the entire model structure. This selective parameter updating reduces computational overhead while still achieving real-time adaptability, as only specific model components need to be adjusted in response to new data.
Solution Approach 2:
The patent implements continuous parameter updates through stochastic gradient descent as data points stream in, maintaining continuous adaptation without interruption. This continuous action allows real-time decision-making while optimizing computational resource usage by processing updates incrementally rather than in batch operations.
3Measurement precision
If parameter estimates are updated using all historical data points, then measurement precision is improved, but memory requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for parameter updates from the data stream, using stochastic gradient descent to compute updates based on current data points rather than storing and processing all historical data. This extraction approach maintains parameter estimation accuracy while minimizing memory requirements by discarding unnecessary historical data.
Solution Approach 2:
The patent treats historical data points as disposable, using them only when needed for stochastic gradient descent updates and then discarding them. This approach allows the model to maintain accurate parameter estimates through sequential processing of data points without retaining large amounts of historical data in memory, thus reducing storage requirements.
Data Source
AI summary
A computer-implemented method for adaptive modeling of a data stream is provided. The method may comprise receiving a plurality of data points forming part of a data stream and providing a predictive model to be fitted to the data stream. The predictive model may be a diffusion model having a plurality of diffusion parameters. A parameter estimate for each one of the diffusion parameters may be updated by obtaining a sample of at least one transition from the data stream and calculating an updated parameter estimate for the diffusion parameter by using a stochastic gradient descent algorithm on the sample. The updating of the parameter estimate may be repeated periodically or in response to one or more further data points being added to the data stream, thereby to permit adaptive estimation of the diffusion parameters of the diffusion model based on dynamics of the data stream.