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 …

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 …

In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural
network architecture is established in this work which combines Multilayer perception and …

In recent years, financial market dynamics forecasting has been a focus of economic
research. To predict the price indices of stock markets, we developed an architecture which …

Forecasting energy market volatility by artificial neural network has long been a focus of
economic research. Based on the discriminatory attitude to the historical price information, a …

In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-
ahead model is developed in this paper which combines empirical mode decomposition …

In this study, we generate 50 Multi-layer Perceptons, 50 Radial Basis Functions, 50 Higher
Order Neural Networks and 50 Recurrent Neural Network and we explore their utility in …

In recent years, artificial neural networks have been employed a lot in forecasting financial
price series. Crude oil and natural gas play the most important role in energy markets …

It is a well-known fact that improving forecasting accuracy is an important yet often
challenging issue. Extensive research has been conducted using neural networks (NNs) to …

Forecasting exchange rates is a subject of wide interest to both academics and practitioners.
We aim at contributing to this vivid research area by highlighting the role of both technical …