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 …
Surveying stock market forecasting techniques–Part II: Soft computing methods
GS Atsalakis, KP Valavanis - Expert Systems with applications, 2009 - Elsevier
The key to successful stock market forecasting is achieving best results with minimum
required input data. Given stock market model uncertainty, soft computing techniques are …
required input data. Given stock market model uncertainty, soft computing techniques are …
A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network
W Sun, C Huang - Journal of Cleaner Production, 2020 - Elsevier
Carbon trading is one of the important mechanisms used to reduce carbon dioxide
emissions. The increasing interest in the carbon trading market has heightened the need to …
emissions. The increasing interest in the carbon trading market has heightened the need to …
Predicting the direction of stock market index movement using an optimized artificial neural network model
M Qiu, Y Song - PloS one, 2016 - journals.plos.org
In the business sector, it has always been a difficult task to predict the exact daily price of the
stock market index; hence, there is a great deal of research being conducted regarding the …
stock market index; hence, there is a great deal of research being conducted regarding the …
Integrating metaheuristics and artificial neural networks for improved stock price prediction
Stock market price is one of the most important indicators of a country's economic growth.
That's why determining the exact movements of stock market price is considerably regarded …
That's why determining the exact movements of stock market price is considerably regarded …
Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models
M Shahvaroughi Farahani, SH Razavi Hajiagha - Soft computing, 2021 - Springer
Today, stock market has important function and it can be a place as a measure of economic
position. People can earn a lot of money and return by investing their money in the stock …
position. People can earn a lot of money and return by investing their money in the stock …
Twenty years of mixture of experts
In this paper, we provide a comprehensive survey of the mixture of experts (ME). We discuss
the fundamental models for regression and classification and also their training with the …
the fundamental models for regression and classification and also their training with the …
Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market
M Qiu, Y Song, F Akagi - Chaos, Solitons & Fractals, 2016 - Elsevier
Accurate prediction of stock market returns is a very challenging task because of the highly
nonlinear nature of the financial time series. In this study, we apply an artificial neural …
nonlinear nature of the financial time series. In this study, we apply an artificial neural …
Grey system theory-based models in time series prediction
Being able to forecast time series accurately has been quite a popular subject for
researchers both in the past and at present. However, the lack of ability of conventional …
researchers both in the past and at present. However, the lack of ability of conventional …
Neural network ensemble operators for time series forecasting
The combination of forecasts resulting from an ensemble of neural networks has been
shown to outperform the use of a single “best” network model. This is supported by an …
shown to outperform the use of a single “best” network model. This is supported by an …