Analysing time series for forecasting (a personal view)
Analysis of an on-off intermittency system with adjustable state levels
We consider a chaotic system with a double-scroll attractor proposed by Elwakil, composing with a second-order system, which has low-dimensional multiple invariant subspaces and multi-level on-off intermittency. This type of composite system always includes a skew-product structure and some invariant subspaces, which are associated with different levels of laminar phase. In order for the level of laminar phase be adjustable, we adopt a nonlinear function with saturation characteristic to tune the...
Analysis of generalized residuals in hazard regression models
Analysis of Simulation Model Aplication to Forecast the Railway Workers Failures
Aplicación de modelos de Wiener-Levy a datos biomédicos.
Para varios conjuntos de datos biomédicos, se quiere hacer inferencia sobre la función media de la población. La mayoría de los métodos clásicos para este tipo de inferencia han sido derivados bajo el supuesto de que los datos constituyen muestras aleatorias de una población normal. Pero en realidad, en el campo médico, pocas veces se tienen muestras aleatorias. El problema práctico principal es hallar un modelo de dependencia entre los datos que aproxime la situación real y que sea manejable desde...
Aplicación de redes neuronales artificiales a la previsión de series temporales no estacionarias o no invertibles.
En los últimos tiempos se ha comprobado un aumento del interés en la aplicación de las Redes Neuronales Artificiales a la previsión de series temporales, intentando explotar las indudables ventajas de estas herramientas. En este artículo se calculan previsiones de series no estacionarias o no invertibles, que presentan dificultades cuando se intentan pronosticar utilizando la metodología ARIMA de Box-Jenkins. Las ventajas de la aplicación de redes neuronales se aprecian con más claridad, cuando...
Application of the random field theory in PET imaging - injection dose optimization
This work presents new application of the random field theory in medical imaging. Results from both integral geometry and random field theory can be used to detect locations with significantly increased radiotracer uptake in images from positron emission tomography (PET). The assumptions needed to use these results are verified on a set of real and simulated phantom images. The proposed method of detecting activation (locations with increased radiotracer concentration) is used to quantify the quality...
Applications of regime-switching models based on aggregation operators
A synthesis of recent development of regime-switching models based on aggregation operators is presented. It comprises procedures for model specification and identification, parameter estimation and model adequacy testing. Constructions of models for real life data from hydrology and finance are presented.
Apport de l'analyse factorielle à l'étude d'un processus
Approximate bias for first-order autoregressive model with uniform innovations. Small sample case
The first-order autoregressive model with uniform innovations is considered. The approximate bias of the maximum likelihood estimator (MLE) of the parameter is obtained. Also, a formula for the approximate bias is given when a single outlier occurs at a specified time with a known amplitude. Simulation procedures confirm that our formulas are suitable. A small sample case is considered only.
Approximate maximum likelihood estimation for a spatial point pattern.
Several authors have proposed stochastic and non-stochastic approximations to the maximum likelihood estimate for a spatial point pattern. This approximation is necessary because of the difficulty of evaluating the normalizing constant. However, it appears to be neither a general theory which provides grounds for preferring a particular method, nor any extensive empirical comparisons. In this paper, we review five general methods based on approximations to the maximum likelihood estimate which have...
Approximation of the fractional Brownian sheet VIA Ornstein-Uhlenbeck sheet
A stochastic “Fubini” lemma and an approximation theorem for integrals on the plane are used to produce a simulation algorithm for an anisotropic fractional Brownian sheet. The convergence rate is given. These results are valuable for any value of the Hurst parameters Finally, the approximation process is iterative on the quarter plane A sample of such simulations can be used to test estimators of the parameters αi,i = 1,2.
Approximations and Time-Discretization in Testing One-Sided Hypotheses for the Mean of a Gaussian Process.
AR models with uniformly distributed noise
AR models are frequently used but usually with normally distributed white noise. In this paper AR model with uniformly distributed white noise are introduces. The maximum likelihood estimation of unknown parameters is treated, iterative method for the calculation of estimates is presented. A numerical example of this procedure and simulation results are also given.
AR(1) processes with given moments of marginal distribution
ARMA models with nonstationary white noise
Artificial neural networks in time series forecasting: a comparative analysis
Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series...
Aspects of analysis of multivariate failure time data.
Multivariate failure time data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated failure time when an individual is followed for the occurrence of two or more types of events for which the individual is simultaneously at risk, or when distinct individuals have depending event times; or more complicated multistate processes where individuals may move among a number of discrete states over...
Asymmetric recursive methods for time series
The problem of asymmetry appears in various aspects of time series modelling. Typical examples are asymmetric time series, asymmetric error distributions and asymmetric loss functions in estimating and predicting. The paper deals with asymmetric modifications of some recursive time series methods including Kalman filtering, exponential smoothing and recursive treatment of Box-Jenkins models.
Asymptotic analysis for bifurcating autoregressive processes via a martingale approach.