  Neural Architecture Search (NAS) methods are widely used in various
industries to obtain high quality taskspecific solutions with minimal human
intervention. Event Sequences find widespread use in various industrial
applications including churn prediction customer segmentation fraud detection
and fault diagnosis among others. Such data consist of categorical and
real-valued components with irregular timestamps. Despite the usefulness of NAS
methods previous approaches only have been applied to other domains images
texts or time series. Our work addresses this limitation by introducing a novel
NAS algorithm SeqNAS specifically designed for event sequence classification.
We develop a simple yet expressive search space that leverages commonly used
building blocks for event sequence classification including multihead self
attention convolutions and recurrent cells. To perform the search we adopt
sequential Bayesian Optimization and utilize previously trained models as an
ensemble of teachers to augment knowledge distillation. As a result of our work
we demonstrate that our method surpasses state of the art NAS methods and
popular architectures suitable for sequence classification and holds great
potential for various industrial applications.
