Over the past few decades, application of artificial neural networks (ANN) to time-series
forecasting (TSF) has been growing rapidly due to several unique features of ANN models …

We propose a simple approach for forecasting univariate time series. The proposed
algorithm is an ensemble learning technique that combines the advice from several …

Stock price forecasting has been mostly realized using quantitative information. However,
recent studies have demonstrated that sentiment information hidden in corporate annual …

This paper proposes a very fast 1-pass-throw-away learning algorithm based on a
hyperellipsoidal function that can be translated and rotated to cover the data set during …

Efficient time series forecasting (TSF) is of utmost importance in order to make better
decision under uncertainty. Over the past few years a large literature has evolved to forecast …

Currently, stock price forecasting is carried out using either time series prediction methods or
trend classifiers. The trend classifiers are designed to predict the behaviour of stock price's …

Probability of withdrawal is a feature of initial public offering (IPOs), which can be an
important parameter in decisions of investors and issuers. Considering the probability of …

Radial basis function (RBF) networks are widely applied in function approximation, system
identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm …

Soft computing is a general term covering a number of methodologies which have
characteristics that make themselves unique in that area. They are tolerant of imprecision …

The motivation for this paper was the introduction of novel short‐term models to trade the
FTSE 100 and DAX 30 exchange‐traded funds (ETF) indices. There are major contributions …