IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance

• URL: http://arxiv.org/abs/2110.07460v1
• Date: Thu, 14 Oct 2021 15:31:16 GMT
• Title: IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance
• Authors: Grace Deng, Cuize Han, Tommaso Dreossi, Clarence Lee, David S. Matteson
• Abstract summary: 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.
• Score: 1.854931308524932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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