JSM 2005 - Toronto

Abstract #303400

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Activity Number: 360
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #303400
Title: Identification of Time-series Models: Application to ARMA Processes
Author(s): Burtschy Bernard and Carole Toque*+
Companies: Telecom Paris University
Address: 46, rue Barrault, Paris, International, 75013, France
Keywords: Time series ; identification ; structural ; PCA ; entropy ; ARMA
Abstract:

In this paper, the proposed method combines the usual approach by autocorrelations and a structural approach, less usual, by analysis of oscillators and theory of information through visualization by factorial methods (principal component analyses PCA and multiple correspondences MCA). It supplies reference graphic models and pertinent criteria for identification and estimation of models and classes. The method is applied to simulated ARMA processes. Based on simulated temporal matrices, first PCA produce good-quality processes representation, with significant groupings and oppositions preserving properties of ARMA autocorrelation functions. PCA becomes a reliable technique in the research of pertinent criteria to identify time-series models. Directly based on autocorrelation matrices, PCA give better results except for "weak" processes, and it ensues first reference graphic models with identification and estimation. Description and measure of possible structural change lead us to introduce oscillators, frequencies, and measures of entropy. This is the structural approach.

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