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A Survey on Deep Learning for Time-Series Forecasting

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Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Part of the book series: Studies in Big Data ((SBD,volume 77))

Abstract

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.

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Abbreviations

ANN:

Artificial Neural Network

AR:

Auto Regression

ARMA:

Auto-Regressive Moving Average

ARIMA:

Auto Regressive Integrated Moving Average

CNN:

Convolutional Neural Network

DBN:

Deep Be-Lief Networks

DL:

Deep learning

GRU:

Gated Recurrent Unit

LSTM:

Long Short Term Memory

MA:

Moving Average

MAE:

Mean Absolute Error

MFE:

Mean Forecast Error

ML:

Machine Learning

MLP:

Multi-Layer Perception

MMSE:

Minimum Mean Square Error

MPE:

The Mean Percentage Error

RBM:

Restricted Boltzmann Machine

RMSE:

The Root Mean Squared Error

SAE:

Stacked-Autoencoders

SARIMA:

Seasonal Autoregressive Integrated Moving Average Models

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  1. Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt

    Amal Mahmoud & Ammar Mohammed

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  1. Amal Mahmoud
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    Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Helwan, Egypt

    Ashraf Darwish

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Mahmoud, A., Mohammed, A. (2021). A Survey on Deep Learning for Time-Series Forecasting. In: Hassanien, A.E., Darwish, A. (eds) Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Studies in Big Data, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-59338-4_19

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