A Survey on Deep Learning for Time-Series Forecasting

• Published in: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges Vol. 77; pp. 365 - 392
• Main Authors: Mahmoud, Amal, Mohammed, Ammar
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Studies in Big Data
• Subjects: Deep learning; Time series
• ISBN: 3030593371; 9783030593377
• ISSN: 2197-6503; 2197-6511
• DOI: 10.1007/978-3-030-59338-4_19

Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech recognition, and text understanding. It is powerful engines capable of learning arbitrary mapping functions, not require a scaled or stationary time series as input, support multivariate inputs, and support multi-step outputs. All of these features together make deep learning useful tools when dealing with more complex time series prediction problems involving large amounts of data, and multiple variables with complex relationships. This paper provides an overview of the most common Deep Learning types for time series forecasting, Explain the relationships between deep learning models and classical approaches to time series forecasting. A brief background of the particular challenges presents in time-series data and the most common deep learning techniques that are often used for time series forecasting is provided. Previous studies that applied deep learning to time series are reviewed.