M
Mehmet Ugur Gudelek
Researcher at TOBB University of Economics and Technology
Publications - 8
Citations - 775
Mehmet Ugur Gudelek is an academic researcher from TOBB University of Economics and Technology. The author has contributed to research in topics: Deep learning & Muzzle. The author has an hindex of 4, co-authored 7 publications receiving 272 citations.
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Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
TL;DR: A comprehensive literature review on DL studies for financial time series forecasting implementations and grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).
Journal ArticleDOI
Deep Learning for Financial Applications : A Survey
TL;DR: This paper tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today, and categorized the works according to their intended subfield in finance but also analyzed them based on their DL models.
Posted Content
Deep Learning for Financial Applications : A Survey
TL;DR: In this article, the authors provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today, and identify possible future implementations and highlighted the pathway for the ongoing research within the field.
Posted Content
Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
TL;DR: In this article, a comprehensive literature review on DL studies for financial time series forecasting implementations is provided, which not only categorized the studies according to their intended forecasting implementation areas, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long Short Term Memory (LSTM).
Posted Content
Deep Learning for Financial Applications: A Survey
TL;DR: In this paper, the authors provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today, and identify possible future implementations and highlighted the pathway for the ongoing research within the field.