[HTML][HTML] An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
H Gunduz - Financial innovation, 2021 - Springer
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
H Gunduz - Financial Innovation, 2021 - cir.nii.ac.jp
抄録< jats: title> Abstract</jats: title>< jats: p> In this study, the hourly directions of eight
banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) …
banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) …
[PDF][PDF] An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
H Gunduz - 2021 - scholar.archive.org
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (Light-GBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (Light-GBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
H Gunduz - Financial Innovation, 2021 - econpapers.repec.org
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
G Hakan - Financial Innovation, 2021 - search.proquest.com
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination.
H Gunduz - Financial Innovation, 2021 - search.ebscohost.com
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
H Gunduz - Financial Innovation, 2021 - ideas.repec.org
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination.
H Gunduz - Financial Innovation, 2021 - go.gale.com
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
H Gunduz - FINANCIAL INNOVATION, 2021 - avesis.kocaeli.edu.tr
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination
H Gunduz - Financial Innovation, 2021 - econstor.eu
In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …
using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models …