Missing Data Imputation for Real Time-series Data in a Steel Industry using Generative Adversarial Networks
Kisan Sarda, Amol Yerudkar, Carmen Del Vecchio
Abstract
On the verge of technology, manufacturing industries revolutionize into smart industries, which create a large amount of multivariate time-series data. However, due to sensors’ failure, extreme environment, etc., the collected data are incomplete and have missing values at several instances that result in an erroneous analysis of the data. The key to resolving this problem is data imputation, i.e., replacing the missing values with synthetic values. In this paper, we introduce a generative adversarial network (GAN) framework to generate the synthetic data pertaining to the data imputation. Over the last decade, GANs have presented excellent results to generate synthetic data for images. By following this stream of research, we consider multivariate time-series data from a steel manufacturing industry and propose a GAN-based data imputation technique. We perform several computer simulations to validate and compare the performance of the proposed GAN method with state-of-the-art data imputation techniques.