Time Series Forecasting With Deep Learning: A Survey

2020/04/30 by Bryan Lim, Stefan Zohren · 100 citations
Computer Science · Decision Sciences · Mathematics · #Forecasting Techniques and Applications #Stock Market Forecasting Methods #Time Series Analysis and Forecasting #cs.LG #stat.ML

paper · pdf · doi:10.1098/rsta.2020.0209

published as Philosophical Transactions of the Royal Society A 2020

arxiv created 2020/09/27 · openalex publication_date 2021/02/15 · arxiv updated 2021/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

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.

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