Synthetic time-series data generation using Generative Adversarial Networks

Abstract

Data, in this new hybrid era, is the driving force for many industries. The automotive industry is no exception to this, and the industry has seen an increasing reliance on data for its operations. The automotive sector, nowadays, offers more services than just selling a vehicle. It now provides complete mobility solutions like connected, shared, and autonomous electric vehicles with personalized options. Such services are possible only with the use of high quality data. Manual data collection from automobiles is an expensive and laborious task, due to which only a sparse amount of high quality data is collected. Reduced data means that the operations and analysis performed are also limited, which makes the company able to offer less services to the customers. One solution to increase the data is to generate it synthetically using deep neural networks. Though there are many methods to generate data synthetically, most of them have limitations on developing diverse data and preserving the temporal dynamics of the original data. This thesis focuses on those issues and studies the possibility of designing a neural network model to generate varied time-series data which has the characteristics of the original data. In this thesis, a Generative Adversarial Network (GAN) is implemented to synthetically generate time-series data. A conventional method of building a machine learning model from scratch is followed in this thesis, after weighing several factors. The relevant training data is collected from the vehicle and then pre-processed to improve the quality of the data. Following this, an initial GAN model is developed that contains the generator and discriminator structure. Then, an enhanced model with supervised learning mechanism called timeGAN model is developed for achieving more realistic synthetic data. This model is then evaluated with suitable metrics, both in a qualitative and quantitative manner. The thesis aims to resolve the issue of scarce data and thus, paves the way for effective predictive maintenance of vehicles and better services to the customers.