Artificial neural networks in business: Two decades of research
M Tkáč, R Verner - Applied Soft Computing, 2016 - Elsevier
In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …
business applications. Despite the growing number of research papers, only few studies …
Conditional time series forecasting with convolutional neural networks
We present a method for conditional time series forecasting based on an adaptation of the
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
A time series analysis-based stock price prediction using machine learning and deep learning models
S Mehtab, J Sen - International Journal of Business …, 2020 - inderscienceonline.com
Prediction of future movement of stock prices has always been a challenging task for
researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is …
researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is …
Impact of COVID‐19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short‐Term Memory
COVID‐19 is an infectious disease that mostly affects the respiratory system. At the time of
this research being performed, there were more than 1.4 million cases of COVID‐19, and …
this research being performed, there were more than 1.4 million cases of COVID‐19, and …
A bat optimized neural network and wavelet transform approach for short-term price forecasting
PMR Bento, JAN Pombo, MRA Calado, S Mariano - Applied energy, 2018 - Elsevier
In the competitive power industry environment, electricity price forecasting is a fundamental
task when market participants decide upon bidding strategies. This has led researchers in …
task when market participants decide upon bidding strategies. This has led researchers in …
Dilated convolutional neural networks for time series forecasting
We present a method for conditional time series forecasting based on an adaptation of the
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
recent deep convolutional WaveNet architecture. The proposed network contains stacks of …
Integration of genetic algorithm with artificial neural network for stock market forecasting
Traditional statistical as well as artificial intelligence techniques are widely used for stock
market forecasting. Due to the nonlinearity in stock data, a model developed using the …
market forecasting. Due to the nonlinearity in stock data, a model developed using the …
[HTML][HTML] Jointly modeling transfer learning of industrial chain information and deep learning for stock prediction
The prediction of stock price has always been a main challenge. The time series of stock
price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid …
price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid …
A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices
D Aggarwal, S Chandrasekaran… - Journal of behavioral and …, 2020 - Elsevier
Bitcoin as an asset class has received phenomenal investor attention and is considered to
have similar characteristics like gold. This study aims to analyze the price behavior of bitcoin …
have similar characteristics like gold. This study aims to analyze the price behavior of bitcoin …
An improved energy management strategy for a hybrid fuel cell/battery passenger vessel
The combination of a fuel cell and an energy storage system for the reduction of fuel
consumption and improving the dynamics of hybrid power systems has successfully been …
consumption and improving the dynamics of hybrid power systems has successfully been …