Extraction of the relevant delays for temporal modeling

Cyril Goutte

doi:10.1109/78.845935

Extraction of the relevant delays for temporal modeling

Cyril Goutte

Research output: Contribution to journal › Journal article › Research › peer-review

501 Downloads (Orbit)

Abstract

When modeling temporal processes, just like in pattern recognition, selecting the optimal number of inputs is of central concern. We take advantage of specific features of temporal modeling to propose a novel method for extracting the inputs that attempts to yield the best predictive performance. The method relies on the use of estimators of the generalization error to assess the predictive performance of the model. This technique is first applied to time series processing, where we perform a number of experiments on synthetic data, as well as a real life dataset, and compare the results to a benchmark physical method. Finally, the method is extended to system identification and illustrated by the estimation of a linear FIR filter on functional magnetic resonance (fMRI) signals.

Original language	English
Journal	IEEE Transactions on Signal Processing
Volume	48
Issue number	6
Pages (from-to)	1787-1795
ISSN	1053-587X
DOIs	https://doi.org/10.1109/78.845935
Publication status	Published - 2000

Bibliographical note

Copyright: 2000 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

Access to Document

10.1109/78.845935

Full textFinal published version, 194 KB

OpenUrl availability

Check availability in DTU Findit

Cite this

@article{9f2bd36d940147fa9453af8da10bde27,

title = "Extraction of the relevant delays for temporal modeling",

abstract = "When modeling temporal processes, just like in pattern recognition, selecting the optimal number of inputs is of central concern. We take advantage of specific features of temporal modeling to propose a novel method for extracting the inputs that attempts to yield the best predictive performance. The method relies on the use of estimators of the generalization error to assess the predictive performance of the model. This technique is first applied to time series processing, where we perform a number of experiments on synthetic data, as well as a real life dataset, and compare the results to a benchmark physical method. Finally, the method is extended to system identification and illustrated by the estimation of a linear FIR filter on functional magnetic resonance (fMRI) signals.",

author = "Cyril Goutte",

note = "Copyright: 2000 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",

year = "2000",

doi = "10.1109/78.845935",

language = "English",

volume = "48",

pages = "1787--1795",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "6",

}

TY - JOUR

T1 - Extraction of the relevant delays for temporal modeling

AU - Goutte, Cyril

N1 - Copyright: 2000 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 - 2000

Y1 - 2000

N2 - When modeling temporal processes, just like in pattern recognition, selecting the optimal number of inputs is of central concern. We take advantage of specific features of temporal modeling to propose a novel method for extracting the inputs that attempts to yield the best predictive performance. The method relies on the use of estimators of the generalization error to assess the predictive performance of the model. This technique is first applied to time series processing, where we perform a number of experiments on synthetic data, as well as a real life dataset, and compare the results to a benchmark physical method. Finally, the method is extended to system identification and illustrated by the estimation of a linear FIR filter on functional magnetic resonance (fMRI) signals.

AB - When modeling temporal processes, just like in pattern recognition, selecting the optimal number of inputs is of central concern. We take advantage of specific features of temporal modeling to propose a novel method for extracting the inputs that attempts to yield the best predictive performance. The method relies on the use of estimators of the generalization error to assess the predictive performance of the model. This technique is first applied to time series processing, where we perform a number of experiments on synthetic data, as well as a real life dataset, and compare the results to a benchmark physical method. Finally, the method is extended to system identification and illustrated by the estimation of a linear FIR filter on functional magnetic resonance (fMRI) signals.

U2 - 10.1109/78.845935

DO - 10.1109/78.845935

M3 - Journal article

SN - 1053-587X

VL - 48

SP - 1787

EP - 1795

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 6

ER -

Extraction of the relevant delays for temporal modeling

Abstract

Bibliographical note

Access to Document

OpenUrl availability

Fingerprint

Cite this