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Time-series forecasting with deep learning: a survey - 2021

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.

Author(s) Name:  Bryan Lim and Stefan Zohren

Journal name:  PHILOSOPHICAL TRANSACTIONS A

Conferrence name:  

Publisher name:  ROYAL SOCIETY PUBLISHING

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

Volume Information:  Volume 379, Issue 2194