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...

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...

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...

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...

It is well known that volatilities and correlations of international stock markets tend to increase in times of financial instability. A dynamic rebalancing scheme is proposed where the underlying market volatility functions as a timing device and portfolio is only rebalanced when the underlying...

The motivation for this paper is to determine the potential economic value of advanced modelling methods for devising trading decision tools for 10-year Government bonds. Two advanced methods are used: time-varying parameter models with the implementation of state space modelling using a Kalman...

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...