Hybridization of hybrid structures for time series forecasting: A review
Achieving the desired accuracy in time series forecasting has become a binding domain,
and develo** a forecasting framework with a high degree of accuracy is one of the most …
and develo** a forecasting framework with a high degree of accuracy is one of the most …
An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO2 emission
Predictive analytics utilizing machine learning algorithms play a pivotal role in various
domains, including the profiling of carbon dioxide (CO2) emissions. This research paper …
domains, including the profiling of carbon dioxide (CO2) emissions. This research paper …
Stock market forecasting using a multi-task approach integrating long short-term memory and the random forest framework
Numerous studies have adopted deep learning (DL) in financial market forecasting models
owing to its superior performance. The DL models require as many relevant input variables …
owing to its superior performance. The DL models require as many relevant input variables …
A new crude oil price forecasting model based on variational mode decomposition
Crude oil price prediction helps to get a better understanding of the global economic
situation. Recently, variational mode decomposition (VMD) is introduced into the field of …
situation. Recently, variational mode decomposition (VMD) is introduced into the field of …
A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction
Financial time series prediction is a hot topic in machine learning field, but existing works
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
A prediction model based on gated nonlinear spiking neural systems
Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing
models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems …
models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems …
A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization
Y Guo, J Guo, B Sun, J Bai, Y Chen - Applied Soft Computing, 2022 - Elsevier
Accurate forecasting of stock prices has been a challenge in the securities market, while the
stock price time series tend to be non-stationary, non-linear, and highly noisy. At present, the …
stock price time series tend to be non-stationary, non-linear, and highly noisy. At present, the …
A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet
Y Huang, C Zhou, K Cui, X Lu - Expert Systems with Applications, 2024 - Elsevier
An increasing number of studies have shown the effectiveness of using deep reinforcement
learning to learn profitable trading strategies from financial market data. However, a single …
learning to learn profitable trading strategies from financial market data. However, a single …
A wind power forecasting method based on optimized decomposition prediction and error correction
J Li, S Zhang, Z Yang - Electric Power Systems Research, 2022 - Elsevier
To reduce the effect of nonlinearity and volatility in the wind power time sequence, a two-
stage short-term wind power forecasting method based on optimized decomposition …
stage short-term wind power forecasting method based on optimized decomposition …
Information fusion-based genetic algorithm with long short-term memory for stock price and trend prediction
Abstract Information fusion is one of the critical aspects in diverse fields of applications;
while the collected data may provide certain perspectives, a fusion of such data can be a …
while the collected data may provide certain perspectives, a fusion of such data can be a …