BOAT: A Bayesian Optimization AutoML Time-series Framework for Industrial Applications

Driven by the increasing degree of automation, industrial production plants have become very data reliant, which poses a great potential for machine learning applications. AutoML is a fledgling research topic that has lately gained much attention in the industrial domain. However, existing applications of AutoML are limited, as industrial systems typically involve time-series data, while AutoML solutions for this type of data seem to be still underrepresented. On the contrary, existing AutoML libraries provide better solutions for, e.g., image, textual, tabular or categorical data. To close this gap to the data types and requirements that are typically found in the industrial domain, especially w.r.t. time-series data, a reusable framework is presented that provides native support for time-series models. The framework is equipped with 1) optimization support for a large number of model and hyperparameter configurations, 2) a warm starting module that performs meta-learning, 3) native support for time-series models, 4) an API for enabling user-defined custom models, and 5) a User Interface that provides a holistic view of the optimization results and deployment instructions. Experimental results show the framework’s competitive performance on time-series data and the effectiveness of the warm starting module in accelerating the optimization procedure. A qualitative analysis of the API is done to showcase the framework’s usability regarding defining custom models.