Automated deep learning for trend prediction in time series data

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, which change over time. 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 automated machine learning techniques to automate the algorithm selection and hyperparameter optimisation process for trend prediction with DNNs. 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 from manual experiments across four datasets.