Research Area:  Machine Learning

Abstract:

Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting—describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, we outline some ways in which deep learning can also facilitate decision support with time-series data.

Keywords:  

Author(s) Name:  Bryan Lim and Stefan Zohren

Publisher name:  ROYAL SOCIETY PUBLISHING

DOI:  https://doi.org/10.1098/rsta.2020.0209

Volume Information:  Volume 379, Issue 2194

Paper Link:   https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0209