Sequential IoT Data Augmentation using Generative Adversarial Networks

TL;DR

This paper explores using GANs to augment sequential IoT data, specifically household energy consumption, demonstrating potential to reduce data collection efforts and improve machine learning model training.

Contribution

It introduces a method for generating sequential IoT data with GANs and proposes a new quantitative evaluation technique for such generated data.

Findings

GANs can generate data subjectively similar to real data

A new quantitative evaluation method for GAN-generated sequential data is proposed

Generated data shows promise for reducing data collection efforts in IoT applications

Abstract

Sequential data in industrial applications can be used to train and evaluate machine learning models (e.g. classifiers). Since gathering representative amounts of data is difficult and time consuming, there is an incentive to generate it from a small ground truth. Data augmentation is a common method to generate more data through a priori knowledge with one specific method, so called generative adversarial networks (GANs), enabling data generation from noise. This paper investigates the possibility of using GANs in order to augment sequential Internet of Things (IoT) data, with an example implementation that generates household energy consumption data with and without swimming pools. The results of the example implementation seem subjectively similar to the original data. Additionally to this subjective evaluation, the paper also introduces a quantitative evaluation technique for GANs if labels are provided. The positive results from the evaluation support the initial assumption that generating sequential data from a small ground truth is possible. This means that tedious data acquisition of sequential data can be shortened. In the future, the results of this paper may be included as a tool in machine learning, tackling the small data challenge.