Bayesian Online Learning for Time-Series Accuracy
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Solution Overview
Problem
Conventional online learning methods for time-series data struggle with accurately adapting to intricate temporal structures, relying on heuristic approaches that fail to capture fluctuations across multiple time-scales.
Innovation Solution
The system performs exact or approximate Bayesian inference of model parameters by representing them as a combination of multiple stochastic processes, allowing for accurate learning of time-series data with intricate temporal structures by updating model weights based on temporal sub-weights characterized as Ornstein-Uhlenbeck processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional online learning methods are used for time-series data, then the system can perform basic model parameter updates, but it fails to accurately capture intricate temporal structures and fluctuations across multiple time-scales
Solution Approach 1:
The patent segments the temporal structure by representing model parameters as a combination of multiple separate stochastic processes, each capturing different time-scale fluctuations. This segmentation allows the system to accurately model intricate temporal patterns by dividing the complex temporal dynamics into manageable stochastic components that can be independently inferred and combined.
2Reliability
If heuristic approaches are used for adapting model parameters, then the system can perform online learning with simple updates, but it cannot accurately learn parameters for data with intricate temporal structures
Solution Approach 1:
The patent implements feedback through Bayesian inference, where the system continuously updates the posterior distribution of model parameters based on incoming time-series data. This feedback mechanism allows the system to refine its parameter estimates over time, accurately capturing intricate temporal structures by incorporating new information while maintaining uncertainty quantification through the probabilistic framework.
3Measurement precision
If exact or approximate Bayesian inference is performed by representing parameters as multiple stochastic processes, then the system can accurately learn time-series data with intricate temporal structures, but the computational complexity increases
Solution Approach 1:
The patent changes the parameter representation from fixed values to time-varying stochastic processes. By modeling parameters as stochastic processes with specific temporal characteristics, the system can capture intricate temporal structures while the Bayesian inference framework provides a systematic way to handle the increased complexity through probabilistic computations that adapt to the temporal dynamics of the data.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for online training of machine learning models predicting time-series data. In one aspect, a method comprises training a machine learning model having a plurality of weights by maintaining weight data, specifying a plurality of sub-weights for each of the plurality of weights and covariance data that estimates the joint uncertainty between the sub-weights, and, at each of a plurality of time steps, receiving model inputs, processing the model inputs using the weight data to generate corresponding model outputs, receiving corresponding ground truth outputs, and updating the weight data using the corresponding ground truth outputs.