Synthetic Time Series Generation via Adversarial Neural Networks
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Solution Overview
Problem
Existing generative models, such as VAEs and GANs, struggle to capture intricate temporal dependencies and high-dimensional structures in time series data, leading to limitations in generating realistic and diverse synthetic time series data.
Innovation Solution
A computer-implemented machine learning system that integrates embedder, recovery, and generator networks, utilizing LSTM layers and time-distributed dense layers to capture temporal dependencies and maintain statistical properties, while employing adversarial training with a discriminator network to enhance data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional approaches such as interpolation or resampling are used for synthetic data generation, then the process is simple and computationally efficient, but the complex dependencies and temporal patterns in time series data are not captured
Solution Approach 1:
The patent replaces traditional mechanical data generation methods (interpolation, resampling) with an intelligent system based on generative adversarial networks. The GAN architecture uses neural networks to learn complex temporal patterns from real data and generate synthetic sequences that preserve these patterns, thereby substituting simple mechanical operations with sophisticated computational modeling.
Solution Approach 2:
The patent transforms the data generation process by changing from deterministic mathematical operations to probabilistic neural network transformations. The generator network learns parameter mappings from real to synthetic data spaces, enabling the system to capture complex temporal dependencies through learned transformations rather than fixed mathematical rules.
2Adaptability or versatility
If existing generative models such as VAEs and GANs are applied to time series data, then data generation capability is enhanced, but the intricate temporal dependencies and high-dimensional structures are not adequately captured
Solution Approach 1:
The patent segments the data generation task into two specialized networks: a generator that creates synthetic time series data and a discriminator that evaluates temporal pattern accuracy. This segmentation allows each network to focus on specific aspects of the problem, with the generator handling data synthesis and the discriminator ensuring temporal dependency preservation through adversarial training.
Solution Approach 2:
The patent implements feedback through the adversarial training mechanism where the discriminator's evaluation of synthetic data quality feeds back to improve the generator's output. The discriminator provides continuous feedback signals that guide the generator to better capture temporal dependencies, creating a closed-loop learning system that progressively improves temporal pattern accuracy.
3Productivity
If existing generative models are used, then synthetic data can be produced, but the statistical properties and long-term correlations of real-world time series data are not preserved
Solution Approach 1:
The patent introduces dynamics through the adversarial training process that continuously adapts both generator and discriminator networks. The system evolves over time, with the discriminator becoming increasingly sophisticated at detecting statistical property violations, which in turn drives the generator to produce more realistic synthetic data that preserves long-term correlations and statistical characteristics.
Solution Approach 2:
The patent creates a composite system combining generative adversarial network architecture with specialized time-series processing components. This composite approach integrates multiple functional elements (generator network, discriminator network, loss functions) that work together to simultaneously achieve high productivity in synthetic data production while maintaining reliability through preservation of statistical properties.
Data Source
AI summary
According to various embodiments, a computer-implemented machine learning system for, and method of, generating synthetic time series data are presented. The system includes: an embedder network that inputs multivariate time series data and produces latent representations capturing temporal dependencies, where the multivariate time series data comprises initial multivariate time series data; a recovery network that produces reconstructed multivariate time series data from the latent representations, where the recovery network employs a plurality of time-distributed dense layers that maintain statistical properties; and a generator network that synthesizes synthetic multivariate time series data from the latent representations, where the synthetic multivariate time series data reflects temporal patterns of the initial multivariate time series data.