Automated Machine Learning for Time Series Prediction

Abstract

Automated Machine Learn (AutoML) process is target of large studies, both from academia and industry. AutoML reduces the demand for data scientists and makes specialists in specific fields able to use Machine Learn (ML) in their domains. An application of ML algorithms is over time-series forecasting, and about these, few works involve the application of AutoML. In this work, an AutoML approach that aggregates time-series forecasting models is proposed. Furthermore, a special focus is given to the optimization stage, which uses genetic algorithm to boost searching for hyper-parameters. In the end, results are compared with a recent time-series forecasting benchmark and we verify that the AutoML model proposed in this work surpasses the benchmark.