Filtering of nonlinear time-series coupled by fractional Gaussian processes

Iñigo Urteaga; Mónica F. Bugallo; Petar M. Djurić

doi:10.1109/CAMSAP.2015.7383843

Filtering of nonlinear time-series coupled by fractional Gaussian processes

Stony Brook University

Stony Brook University

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

Abstract

In this paper we consider a set of time-series that are coupled by latent fractional Gaussian processes. Specifically, we address time-series that combine idiosyncratic short-term and shared long-term features. The long-memory is modeled by fractional Gaussian processes, whereas the short-memory properties are captured by linear models of past data. The observations are nonlinear functions of the latent states and therefore, for inference of the latent states we resort to a sequential Monte Carlo sampling technique. The proposed solution is evaluated via simulations of an illustrative practical scenario.

Original language	English
Title of host publication	2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	489-492
Number of pages	4
ISBN (Electronic)	9781479919635
DOIs	https://doi.org/10.1109/CAMSAP.2015.7383843
State	Published - 2015
Event	6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico Duration: Dec 13 2015 → Dec 16 2015

Publication series

Name	2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

Conference

Conference	6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Country/Territory	Mexico
City	Cancun
Period	12/13/15 → 12/16/15

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10.1109/CAMSAP.2015.7383843

Cite this

Urteaga, I., Bugallo, M. F., & Djurić, P. M. (2015). Filtering of nonlinear time-series coupled by fractional Gaussian processes. In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 (pp. 489-492). Article 7383843 (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP.2015.7383843

Urteaga, Iñigo ; Bugallo, Mónica F. ; Djurić, Petar M. / Filtering of nonlinear time-series coupled by fractional Gaussian processes. 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 489-492 (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015).

@inproceedings{98a5e2524d1040f1bd80059e14cc296b,

title = "Filtering of nonlinear time-series coupled by fractional Gaussian processes",

abstract = "In this paper we consider a set of time-series that are coupled by latent fractional Gaussian processes. Specifically, we address time-series that combine idiosyncratic short-term and shared long-term features. The long-memory is modeled by fractional Gaussian processes, whereas the short-memory properties are captured by linear models of past data. The observations are nonlinear functions of the latent states and therefore, for inference of the latent states we resort to a sequential Monte Carlo sampling technique. The proposed solution is evaluated via simulations of an illustrative practical scenario.",

author = "I{\~n}igo Urteaga and Bugallo, \{M{\'o}nica F.\} and Djuri{\'c}, \{Petar M.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 ; Conference date: 13-12-2015 Through 16-12-2015",

year = "2015",

doi = "10.1109/CAMSAP.2015.7383843",

language = "English",

series = "2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015",

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

pages = "489--492",

booktitle = "2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015",

}

Urteaga, I, Bugallo, MF & Djurić, PM 2015, Filtering of nonlinear time-series coupled by fractional Gaussian processes. in 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015., 7383843, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015, Institute of Electrical and Electronics Engineers Inc., pp. 489-492, 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015, Cancun, Mexico, 12/13/15. https://doi.org/10.1109/CAMSAP.2015.7383843

Filtering of nonlinear time-series coupled by fractional Gaussian processes. / Urteaga, Iñigo; Bugallo, Mónica F.; Djurić, Petar M.
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 489-492 7383843 (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015).

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

TY - GEN

T1 - Filtering of nonlinear time-series coupled by fractional Gaussian processes

AU - Urteaga, Iñigo

AU - Bugallo, Mónica F.

AU - Djurić, Petar M.

PY - 2015

Y1 - 2015

N2 - In this paper we consider a set of time-series that are coupled by latent fractional Gaussian processes. Specifically, we address time-series that combine idiosyncratic short-term and shared long-term features. The long-memory is modeled by fractional Gaussian processes, whereas the short-memory properties are captured by linear models of past data. The observations are nonlinear functions of the latent states and therefore, for inference of the latent states we resort to a sequential Monte Carlo sampling technique. The proposed solution is evaluated via simulations of an illustrative practical scenario.

AB - In this paper we consider a set of time-series that are coupled by latent fractional Gaussian processes. Specifically, we address time-series that combine idiosyncratic short-term and shared long-term features. The long-memory is modeled by fractional Gaussian processes, whereas the short-memory properties are captured by linear models of past data. The observations are nonlinear functions of the latent states and therefore, for inference of the latent states we resort to a sequential Monte Carlo sampling technique. The proposed solution is evaluated via simulations of an illustrative practical scenario.

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U2 - 10.1109/CAMSAP.2015.7383843

DO - 10.1109/CAMSAP.2015.7383843

M3 - Conference contribution

T3 - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

SP - 489

EP - 492

BT - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

Y2 - 13 December 2015 through 16 December 2015

ER -

Urteaga I, Bugallo MF , Djurić PM. Filtering of nonlinear time-series coupled by fractional Gaussian processes. In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 489-492. 7383843. (2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015). doi: 10.1109/CAMSAP.2015.7383843

Filtering of nonlinear time-series coupled by fractional Gaussian processes

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