  Recently, Deep Neural Network (DNN) algorithms have been explored for
predicting trends in time series data. In many real world applications, time
series data are captured from dynamic systems. DNN models must provide stable
performance when they are updated and retrained as new observations becomes
available. In this work we explore the use of automatic machine learning
techniques to automate the algorithm selection and hyperparameter optimisation
process for trend prediction. We demonstrate how a recent AutoML tool,
specifically the HpBandSter framework, can be effectively used to automate DNN
model development. Our AutoML experiments found optimal configurations that
produced models that compared well against the average performance and
stability levels of configurations found during the manual experiments across
four data sets.
