Result: Managing distribution changes in time series prediction

Title:
Managing distribution changes in time series prediction
Authors:
MATIAS, J. M, GONZALEZ-MANTEIGA, W, TABOADA, J, ORDONEZ, C
Source:
The international conference on computational methods in sciences and engineering 2004Journal of computational and applied mathematics. 191(2):206-215
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 10 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Théorie des probabilités et processus stochastiques, Probability theory and stochastic processes, Lois de probabilités, Distribution theory, Statistiques, Statistics, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Applications, Assurances, économie, finance, Insurance, economics, finance, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse numérique, Numerical analysis, Análisis numérico, Distribution temporelle, Time distribution, Distribución temporal, Fonction répartition, Distribution function, Función distribución, Hétéroscedasticité, Heteroscedasticity, Heteroscedasticidad, Marché valeurs, Stock markets, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Maximisation, Maximization, Maximización, Prédiction, Prediction, Predicción, Sélection modèle, Model selection, Selección modelo, Série temporelle, Time series, Serie temporal, Loi hypernormale, Hypernormal distribution, Modèle GARCH, GARCH model, Transformation Box Cox, Box Cox tranformation, 02.50.Ng, 05.45.Tp, 07.05.Mh, 89.65.Gh Box-Cox transformations, GARCH, Genetic algorithms, Heteroskedasticity
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, University of Vigo, 36200 Vigo, Spain
Department of Statistics, University of Santiago de Compostela, Spain
Department of Natural Resources, University of Vigo, Spain
ISSN:
0377-0427
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17689316
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.17689316
Database:
PASCAL Archive

Further Information

When a problem is modeled statistically, a single distribution model is usually postulated that is assumed to be valid for the entire space. Nonetheless, this practice may be somewhat unrealistic in certain application areas, in which the conditions of the process that generates the data may change; as far as we are aware, however, no techniques have been developed to tackle this problem. This article proposes a technique for modeling and predicting this change in time series with a view to improving estimates and predictions. The technique is applied, among other models, to the hypernormal distribution recently proposed. When tested on real data from a range of stock market indices the technique produces better results that when a single distribution model is assumed to be valid for the entire period of time studied. Moreover, when a global model is postulated, it is highly recommended to select the hypernormal distribution parameter in the same likelihood maximization process.