Lag space estimation in time series modelling

Cyril Goutte

doi:10.1109/ICASSP.1997.595502

Lag space estimation in time series modelling

Cyril Goutte

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer in a neural network. We give a rough description of the problem, insist on the concept of generalisation, and propose a generalisation-based method. We compare it to a non-parametric test, and carry out experiments, both on the well-known Henon map, and on a real data set

Original language	English
Title of host publication	1997 Intl. Conference on Acoustics, Speech, and Signal Processing
Place of Publication	Piscataway,New Jersey
Publisher	IEEE
Publication date	1997
Pages	3313-3316
ISBN (Print)	0-8186-7919-0
DOIs	https://doi.org/10.1109/ICASSP.1997.595502
Publication status	Published - 1997
Event	1997 IEEE International Conference on Acoustics, Speech, and Signal Processing - Munich, Germany Duration: 21 Apr 1997 → 24 Apr 1997 https://ieeexplore.ieee.org/xpl/conhome/4635/proceeding

Conference

Conference	1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
Country/Territory	Germany
City	Munich
Period	21/04/1997 → 24/04/1997
Internet address	https://ieeexplore.ieee.org/xpl/conhome/4635/proceeding

Bibliographical note

Copyright: 1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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title = "Lag space estimation in time series modelling",

abstract = "The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer in a neural network. We give a rough description of the problem, insist on the concept of generalisation, and propose a generalisation-based method. We compare it to a non-parametric test, and carry out experiments, both on the well-known Henon map, and on a real data set",

author = "Cyril Goutte",

note = "Copyright: 1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE ICASSP'97 ; Conference date: 21-04-1997 Through 24-04-1997",

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N1 - Copyright: 1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 1997

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N2 - The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer in a neural network. We give a rough description of the problem, insist on the concept of generalisation, and propose a generalisation-based method. We compare it to a non-parametric test, and carry out experiments, both on the well-known Henon map, and on a real data set

AB - The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer in a neural network. We give a rough description of the problem, insist on the concept of generalisation, and propose a generalisation-based method. We compare it to a non-parametric test, and carry out experiments, both on the well-known Henon map, and on a real data set

U2 - 10.1109/ICASSP.1997.595502

DO - 10.1109/ICASSP.1997.595502

M3 - Article in proceedings

SN - 0-8186-7919-0

SP - 3313

EP - 3316

BT - 1997 Intl. Conference on Acoustics, Speech, and Signal Processing

PB - IEEE

CY - Piscataway,New Jersey

T2 - 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 21 April 1997 through 24 April 1997

ER -

Lag space estimation in time series modelling

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