Ai in finance: challenges, techniques, and opportunities
L Cao - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
AI in finance refers to the applications of AI techniques in financial businesses. This area has
attracted attention for decades, with both classic and modern AI techniques applied to …
attracted attention for decades, with both classic and modern AI techniques applied to …
A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM
Y Liang, Y Lin, Q Lu - Expert Systems with Applications, 2022 - Elsevier
Gold price has always played an important role in the world economy and finance. In order
to predict the gold price more accurately, this paper proposes a novel decomposition …
to predict the gold price more accurately, this paper proposes a novel decomposition …
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 …
Multi-source aggregated classification for stock price movement prediction
Predicting stock price movements is a challenging task. Previous studies mostly used
numerical features and news sentiments of target stocks to predict stock price movements …
numerical features and news sentiments of target stocks to predict stock price movements …
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 …
NILM applications: Literature review of learning approaches, recent developments and challenges
This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
Stock price prediction using deep learning and frequency decomposition
H Rezaei, H Faaljou, G Mansourfar - Expert Systems with Applications, 2021 - Elsevier
Nonlinearity and high volatility of financial time series have made it difficult to predict stock
price. However, thanks to recent developments in deep learning and methods such as long …
price. However, thanks to recent developments in deep learning and methods such as long …
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 …
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …