  Classification of large multivariate time series with strong class imbalance
is an important task in real-world applications. Standard methods of class
weights, oversampling, or parametric data augmentation do not always yield
significant improvements for predicting minority classes of interest.
Non-parametric data augmentation with Generative Adversarial Networks (GANs)
offers a promising solution. We propose Imputation Balanced GAN (IB-GAN), a
novel method that joins data augmentation and classification in a one-step
process via an imputation-balancing approach. IB-GAN uses imputation and
resampling techniques to generate higher quality samples from randomly masked
vectors than from white noise, and augments classification through a
class-balanced set of real and synthetic samples. Imputation hyperparameter
$p_{miss}$ allows for regularization of classifier variability by tuning
innovations introduced via generator imputation. IB-GAN is simple to train and
model-agnostic, pairing any deep learning classifier with a
generator-discriminator duo and resulting in higher accuracy for under-observed
classes. Empirical experiments on open-source UCR data and proprietary 90K
product dataset show significant performance gains against state-of-the-art
parametric and GAN baselines.
