The aim of this paper is to present a method able to graphically describe the amount of structure in a time series. In the following, 'structure' is defined as the extent to which a time series is either trending or mean-reverting (that is showing pockets of positive as well as negative...

The purpose of this paper is twofold. Firstly, to assess the merit of estimating probability density functions rather than level or classification estimations on a one-day-ahead forecasting task of the EUR|USD time series. This is implemented using a Gaussian mixture model neural network,...

The purpose of this paper is twofold. Firstly, to assess the merit of estimating probability density functions rather than level or direction forecasts for one-day-ahead forecasts of the Morgan Stanley Technology Index Tracking Fund (MTK). This is implemented using a Gaussian mixture model...

Conditional independence graphs are now widely applied in science and industry to display interactions between large numbers of variables. However, the computational load of structure identification grows with the number of nodes in the network and the sample size. A tailored version of the PC...

In this paper, we examine the management of unintentional dwelling fire risk through the development of a geographical information system (GIS) for dwelling fire prevention support based upon an 18-month case study in a UK fire and rescue service. Previous research into causal factors in...

A clear motivation for this paper is the investigation of a correlation filter to improve the return/risk performance of spread trading models. A further motivation for this paper is the extension of trading futures spreads beyond the 'Fair Value' type of model used by Butterworth and Holmes...

In this article, a mixed methodology that combines both the Autoregressive Moving Average Model (ARMA) and Neural Network Regression (NNR) models is proposed to take advantage of the unique strength of ARMA and NNR models in linear and nonlinear modelling. Experimental results with real data...

In the current paper, we present an integrated genetic programming (GP) environment called java GP modelling. The java GP modelling environment is an implementation of the steady-state GP algorithm. This algorithm evolves tree-based structures that represent models of inputs and outputs. The...

The motivation for this article is the investigation of the use of a promising class of neural network (NN) models, higher order neural networks (HONNs), when applied to the task of forecasting and trading the 21-day-ahead realised volatility of the FTSE 100 futures index. This is done by...