  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.
