Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3838
Title: Financial time series forecasting with deep learning : A systematic literature review: 2005-2019
Authors: Sezer, Ömer Berat
Güdelek, Mehmet Uğur
Özbayoğlu, Ahmet Murat
Keywords: Deep learning
finance
computational intelligence
machine learning
time series forecasting
CNN
LSTM
RNN
Issue Date: May-2020
Publisher: Elsevier Ltd
Source: Sezer, O. B., Gudelek, M. U. and Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181.
Abstract: Financial time series forecasting is undoubtedly the top choice of computational intelligence for finance researchers in both academia and the finance industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers have created various models, and a vast number of studies have been published accordingly. As such, a significant number of surveys exist covering ML studies on financial time series forecasting. Lately, Deep Learning (DL) models have appeared within the field, with results that significantly outperform their traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting, there is a lack of review papers that solely focus on DL for finance. Hence, the motivation of this paper is to provide a comprehensive literature review of DL studies on financial time series forecasting implementation. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, and commodity forecasting, but we also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM). We also tried to envision the future of the field by highlighting its possible setbacks and opportunities for the benefit of interested researchers. (C) 2020 Elsevier B.V. All rights reserved.
URI: https://hdl.handle.net/20.500.11851/3838
https://www.sciencedirect.com/science/article/pii/S1568494620301216?via%3Dihub
https://ui.adsabs.harvard.edu/abs/2019arXiv191113288B/abstract
ISSN: 1568-4946
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

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