Several neural network models have been proposed in the literature to predict the future
behavior of financial time series. However, an intrinsic limitation arises from this particular …

In this paper a novel application of a particular type of spiking neural network, a
Polychronous Spiking Network, was used for financial time series prediction. It is argued that …

Time series forecasting by fuzzy inference systems based on optimal non-uniform attractor
embedding in the multidimensional delay phase space is analyzed in this paper. A near …

In this paper we propose a hybrid methodology to design morphological-rank-linear (MRL)
perceptrons in the problem of software development cost estimation (SDCE). In this …

This study seeks to investigate the various training functions with non-linear auto regressive
eXogenous neural network (NARXNN) to forecasting the closing index of the stock market …

In this paper, we present a method to overcome the random walk (RW) dilemma for financial
time series forecasting, called swarm-based translation-invariant morphological prediction …

Recurrent neural networks are dynamical systems that provide for memory capabilities to
recall past behaviour, which is necessary in the prediction of time series. In this paper, a …

There are many researches on forecasting time series for building trading systems for
financial markets. Some of these studies have shown that it is possible to obtain satisfactory …

Self-organizing neurofuzzy approaches have matured in their online learning of fuzzy-
associative structures under time-invariant conditions. To maximize their operative value for …

In this work a class of hybrid morphological perceptrons, called dilation–erosion perceptron
(DEP), is presented to overcome the random walk dilemma in the time series forecasting …