Applications of deep learning in stock market prediction: recent progress

W Jiang - Expert Systems with Applications, 2021 - Elsevier
Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

A survey on machine learning for stock price prediction: Algorithms and techniques

M Obthong, N Tantisantiwong, W Jeamwatthanachai… - 2020 - eprints.soton.ac.uk
Stock market trading is an activity in which investors need fast and accurate information to
make effective decisions. Since many stocks are traded on a stock exchange, numerous …

Forecasting cryptocurrency prices using LSTM, GRU, and bi-directional LSTM: a deep learning approach

PL Seabe, CRB Moutsinga, E Pindza - Fractal and Fractional, 2023 - mdpi.com
Highly accurate cryptocurrency price predictions are of paramount interest to investors and
researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to …

Neural network based country wise risk prediction of COVID-19

R Pal, AA Sekh, S Kar, DK Prasad - Applied Sciences, 2020 - mdpi.com
The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new
challenges to the research community. Artificial intelligence (AI) driven methods can be …

A deep neural network model for speaker identification

F Ye, J Yang - Applied Sciences, 2021 - mdpi.com
Speaker identification is a classification task which aims to identify a subject from a given
time-series sequential data. Since the speech signal is a continuous one-dimensional time …

EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

X Peng, H Wang, J Lang, W Li, Q Xu, Z Zhang, T Cai… - Energy, 2021 - Elsevier
Effective wind-power prediction enhances the adaptability of a wind power system to the
instability of wind power, which is beneficial for load and frequency regulation, helping to …

Short‐Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

EB Wegayehu, FB Muluneh - Advances in Meteorology, 2022 - Wiley Online Library
Hydrological forecasting is one of the key research areas in hydrology. Innovative
forecasting tools will reform water resources management systems, flood early warning …

Performance improvement of LSTM-based deep learning model for streamflow forecasting using Kalman filtering

F Bakhshi Ostadkalayeh, S Moradi, A Asadi… - Water Resources …, 2023 - Springer
Prediction of streamflow as a crucial source of hydrological information plays a central role
in various fields of water resources projects. While accurate daily streamflow forecasts are …

GRU neural network based on CEEMDAN–wavelet for stock price prediction

C Qi, J Ren, J Su - Applied Sciences, 2023 - mdpi.com
Stock indices are considered to be an important indicator of financial market volatility in
various countries. Therefore, the stock market forecast is one of the challenging issues to …

Hybrid information mixing module for stock movement prediction

J Choi, S Yoo, X Zhou, Y Kim - IEEE Access, 2023 - ieeexplore.ieee.org
With the continuing active research on deep learning, research on stock price prediction
using deep learning has been actively conducted in the financial industry. This paper …