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Multivariate ARIMA Compositional Time Series Analysis

A compositional time series is obtained when a compositional data vector is observed at different points in time. Inherently, then, a compositional time series is a multivariate time series with important constraints on the variables observed at any instance in time. Although this type of data frequently occurs in situations of real practical interest, a trawl through the statistical literature reveals that research in the field is very much in its infancy and that many theoretical and empirical issues still remain to be addressed. Any appropriate statistical methodology for the analysis of compositional time series must take into account the constraints which are not allowed for by the usual statistical techniques available for analysing multivariate time series. One general approach to analyzing compositional time series consists in the application of an initial transform to break the positive and unit sum constraints, followed by the analysis of the transformed time series using multivariate ARIMA models. In this paper we discuss the use of the additive log-ratio, centred log-ratio and isometric log-ratio transforms. We also present results from an empirical study designed to explore how the selection of the initial transform affects subsequent multivariate ARIMA modelling as well as the quality of the forecasts

Geologische Vereinigung; Institut d鈥橢stad铆stica de Catalunya; International Association for Mathematical Geology; C脿tedra Llu铆s Santal贸 d鈥橝plicacions de la Matem脿tica; Generalitat de Catalunya, Departament d鈥橧nnovaci贸, Universitats i Recerca; Ministerio de Educaci贸n y Ciencia; Ingenio 2010.

Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada

Manager: Daunis i Estadella, Josep
Mart铆n Fern谩ndez, Josep Antoni
Other contributions: Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada
Author: Aguilar, Luc铆a
Barcel贸 i Vidal, Carles
Date: 2008 May 29
Abstract: A compositional time series is obtained when a compositional data vector is observed at different points in time. Inherently, then, a compositional time series is a multivariate time series with important constraints on the variables observed at any instance in time. Although this type of data frequently occurs in situations of real practical interest, a trawl through the statistical literature reveals that research in the field is very much in its infancy and that many theoretical and empirical issues still remain to be addressed. Any appropriate statistical methodology for the analysis of compositional time series must take into account the constraints which are not allowed for by the usual statistical techniques available for analysing multivariate time series. One general approach to analyzing compositional time series consists in the application of an initial transform to break the positive and unit sum constraints, followed by the analysis of the transformed time series using multivariate ARIMA models. In this paper we discuss the use of the additive log-ratio, centred log-ratio and isometric log-ratio transforms. We also present results from an empirical study designed to explore how the selection of the initial transform affects subsequent multivariate ARIMA modelling as well as the quality of the forecasts
Geologische Vereinigung; Institut d鈥橢stad铆stica de Catalunya; International Association for Mathematical Geology; C脿tedra Llu铆s Santal贸 d鈥橝plicacions de la Matem脿tica; Generalitat de Catalunya, Departament d鈥橧nnovaci贸, Universitats i Recerca; Ministerio de Educaci贸n y Ciencia; Ingenio 2010.
Format: application/pdf
Citation: Aguilar, L.; Barcel贸 Vidal, C. 鈥橫ultivariate ARIMA Compositional Time Series Analysis鈥 a CODAWORK鈥08. Girona: La Universitat, 2008 [consulta: 12 maig 2008]. Necessita Adobe Acrobat. Disponible a Internet a: http://hdl.handle.net/10256/722
Document access: http://hdl.handle.net/10256/722
Language: eng
Publisher: Universitat de Girona. Departament d鈥橧nform脿tica i Matem脿tica Aplicada
Rights: Tots els drets reservats
Subject: Estad铆stica matem脿tica
Title: Multivariate ARIMA Compositional Time Series Analysis
Type: info:eu-repo/semantics/conferenceObject
Repository: DUGiDocs

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