Dynamic System Foundation Models for Synthetic Time-Series Training
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Solution Overview
Problem
Measuring and obtaining extended time series data for physical systems is complex and expensive, and existing methods struggle to effectively generate labeled training data for dynamic systems, leading to overfitting and inefficiencies in predictive modeling.
Innovation Solution
A computer-implemented method using a dynamic system dictionary, encoder, and noise generator to classify and generate time-series data through constraint learning and diffusion decoding, allowing for the creation of new time-series data that mimic the dynamics of input samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extended time series data is obtained through direct measurement of physical systems, then data accuracy is improved, but measurement complexity and cost increase
Solution Approach 1:
The patent creates synthetic copies of time series data by training a foundational model on available measurement data and using it to generate additional training samples. This copying approach provides accurate training data without requiring complex extended measurements of the actual physical system.
2Reliability
If more extended time series data is collected for training, then model prediction accuracy is improved, but data acquisition time and resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training a foundational model on available data before it is needed for specific prediction tasks. This pre-training creates a reusable model that can be quickly adapted to various downstream tasks without requiring extensive data collection each time predictions are needed.
Solution Approach 2:
The foundational model generates synthetic copies of time series data that can be used for training task-specific models. This eliminates the need to collect extensive additional data for each prediction task, significantly reducing data acquisition time while maintaining prediction accuracy.
3Adaptability or versatility
If a general foundational model is trained on diverse data, then model versatility is improved, but training data requirements and computational resources increase
Solution Approach 1:
The patent creates a universal foundational model that can serve multiple downstream prediction tasks across different physical systems. This single model replaces the need for separate models for each task, achieving versatility while managing training data requirements through efficient pre-training on diverse but relevant time series data.
Data Source
AI summary
An approach for generating time-series dynamic-system training data. The approach may comprise providing a plurality of dynamic systems to a dynamic system dictionary. Where the dynamical system dictionary may comprise a library of functions. The approach may further comprise classifying each of the plurality of dynamic systems. Where classifying may comprise, generating a hierarchical dynamic system data, based on constraint learning, with an encoder and noise generator. The approach may further comprise training a diffusion decoder to generate a time-series segment, based on the classified plurality of dynamical systems. Further, the approach may comprise providing a first time series data segment and generating time-series dynamical training data based on the diffusion decoder using the first time-series data segment as input.