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Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks

Published

Author(s)

Adam Wunderlich, Jack Sklar

Abstract

Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for data-driven modeling, it is important to determine to what extent GANs can faithfully reproduce noise in target data sets. In this paper, we present an empirical investigation that aims to shed light on this issue for time series. Namely, we assess two general-purpose GANs for time series that are based on the popular deep convolutional GAN (DCGAN) architecture, a direct time-series model and an image-based model that uses a short-time Fourier transform (STFT) data representation. The GAN models are trained and quantitatively evaluated using distributions of simulated noise time series with known ground-truth parameters. Target time series distributions include a broad range of noise types commonly encountered in physical measurements, electronics, and communication systems: band-limited thermal noise, power law noise, shot noise, and impulsive noise. We find that GANs are capable of learning many noise types, although they predictably struggle when the GAN architecture is not well suited to some aspects of the noise, e.g., impulsive time-series with extreme outliers. Our findings provide insights into the capabilities and potential limitations of current approaches to time-series GANs and highlight areas for further research. In addition, our battery of tests provides a useful benchmark to aid the development of deep generative models for time series.
Citation
Machine Learning: Science and Technology
Volume
4
Issue
3

Keywords

time series, machine learning, colored noise, fractional noise, shot noise, impulsive noise

Citation

Wunderlich, A. and Sklar, J. (2023), Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks, Machine Learning: Science and Technology, [online], https://doi.org/10.1088/2632-2153/acee44, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934800 (Accessed May 19, 2024)

Issues

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Created August 21, 2023