Abstract This paper presents a framework for the development of computationally-efficient surrogate model air pollution dispersion. Numerical simulation dispersion is fundamental importance mitigation in Seveso-type accidents, and, extreme cases, design evacuation scenarios which long-range forecasting necessary. Due to high computational load, sophisticated programs are not always useful promp...

The potential use of functional evolutionary models and mechanistic ecological models as predictive tools in fisheries ecology is discussed. Evolution by natural selection is a force that leads to ecological adaptations of the individuals in the populations. By mechanistic modelling of the sense organs, we may model how individuals perceive their environment, and by life history theory we may p...

This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX) and neural network (NN) techniques. The data used for this re...

This paper considers the problem of modelling and forecasting for multivariate financial time series. The use of Dynamic Linear State Space models and Stochastic Volatility models with Kalman filtering techniques to address this problem is considered in the context of providing a modular software implementation. The combination of these two approaches is presented with an illustrative example. ...

Abstract While there has been a great deal of interest in the modelling of non-linearities and regime shifts in economic time series, there is no clear consensus regarding the forecasting abilities of these models. In this paper we develop a general approach to predict multiple time series subject to Markovian shifts in the regime. The feasibility of the proposed forecasting techniques in empir...

Topic of this contribution is the integration of data uncertainty in complex analysis processes. By means of a flooding forecast as an example application, the enhancements which are necessary in data modelling and analysis functions are explained. The major objective of the research is to determine the uncertainty in the spatial extension of the possible flooded region. In particular, the impa...

The purpose of this paper is to forecast the time evolution of a binary response variable from an associated continuous time series observed only at discrete time points that usually are unequally spaced. In order to solve this problem we are going to use a functional logit model based on functional principal component analysis of the predictor time series that takes into account its continuous...

In this article, we illustrate the effect of imposing symmetry as prior knowledge into the modelling stage, within the context of chaotic time series predictions. It is illustrated that using Least-Squares Support Vector Machines with symmetry constraints improves the simulation performance, for the cases of time series generated from the Lorenz attractor, and multi-scroll attractors. Not only ...

1. Introduction Economy can be thought as a complex system where aggregate behaviour is determined by the interaction between individuals at the micro level [1]. This paper presents an overview of the rapidly growing field of Agent-based Modelling (ABM) that seeks to explain and forecast market price movements using a bottom-up approach. Extensive literature reviews can be found elsewhere [2-4]...