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Denoising Time Series Data Using Asymmetric Generative Adversarial Networks

  • Sunil Gandhi
  • Tim Oates
  • Tinoosh Mohsenin
  • David Hairston
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)

Abstract

Denoising data is a preprocessing step for several time series mining algorithms. This step is especially important if the noise in data originates from diverse sources. Consequently, it is commonly used in biomedical applications that use Electroencephalography (EEG) data. In EEG data noise can occur due to ocular, muscular and cardiac activities. In this paper, we explicitly learn to remove noise from time series data without assuming a prior distribution of noise. We propose an online, fully automated, end-to-end system for denoising time series data. Our model for denoising time series is trained using unpaired training corpora and does not need information about the source of the noise or how it is manifested in the time series. We propose a new architecture called AsymmetricGAN that uses a generative adversarial network for denoising time series data. To analyze our approach, we create a synthetic dataset that is easy to visualize and interpret. We also evaluate and show the effectiveness of our approach on an existing EEG dataset.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sunil Gandhi
    • 1
  • Tim Oates
    • 1
  • Tinoosh Mohsenin
    • 1
  • David Hairston
    • 2
  1. 1.University of Maryland, Baltimore CountyBaltimoreUSA
  2. 2.U.S. Army Research LaboratoryAdelphiUSA

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