Automatic deep learning for trend prediction in time series data

TL;DR

This paper explores the use of AutoML, specifically HpBandSter, to automate deep learning model selection and hyperparameter tuning for trend prediction in time series data, demonstrating improved stability and performance.

Contribution

It introduces an AutoML approach for automating DNN development in time series trend prediction, enhancing model stability and performance compared to manual tuning.

Findings

AutoML with HpBandSter effectively automates DNN configuration.

Automated models perform comparably or better than manually tuned models.

The approach improves model stability across multiple datasets.

Abstract

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.