Financial time series forecasting with deep learning: A systematic literature review: 2005–2019
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 …
for finance researchers in both academia and the finance industry due to its broad …
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 …
both economists and computer scientists. With the purpose of building an effective prediction …
Frequency-domain MLPs are more effective learners in time series forecasting
Time series forecasting has played the key role in different industrial, including finance,
traffic, energy, and healthcare domains. While existing literatures have designed many …
traffic, energy, and healthcare domains. While existing literatures have designed many …
Spectral temporal graph neural network for multivariate time-series forecasting
Multivariate time-series forecasting plays a crucial role in many real-world applications. It is
a challenging problem as one needs to consider both intra-series temporal correlations and …
a challenging problem as one needs to consider both intra-series temporal correlations and …
Deep learning for financial applications: A survey
Computational intelligence in finance has been a very popular topic for both academia and
financial industry in the last few decades. Numerous studies have been published resulting …
financial industry in the last few decades. Numerous studies have been published resulting …
FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective
Multivariate time series (MTS) forecasting has shown great importance in numerous
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …
[BOK][B] Neural networks and deep learning
CC Aggarwal - 2018 - Springer
“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …
McDonald Neural networks were developed to simulate the human nervous system for …
A systematic review of deep transfer learning for machinery fault diagnosis
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions
The stock market has been an attractive field for a large number of organizers and investors
to derive useful predictions. Fundamental knowledge of stock market can be utilised with …
to derive useful predictions. Fundamental knowledge of stock market can be utilised with …
Temporal relational ranking for stock prediction
Stock prediction aims to predict the future trends of a stock in order to help investors make
good investment decisions. Traditional solutions for stock prediction are based on time …
good investment decisions. Traditional solutions for stock prediction are based on time …