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Journal articles

Identification of AR Time-series based on binary data

Romain Auber 1 Mathieu Pouliquen 1 Eric Pigeon 1 Olivier Gehan 1 Mohammed M'Saad 1 Pierre Alexandre Chapon 2 Sébastien Moussay 3
1 LAC - Laboratoire d'automatique de Caen
2 BodyCap Le compagnon santé connecté
3 INSERM - Institut National de la Santé et de la Recherche Médicale
Abstract : In this study, the authors consider the identification of auto-regressive (AR) models for time-series from one-bit quantised observation sequences. The only available information is the fact that the samples of the time-series are lower or higher than a threshold of quantisation. This threshold may be different from zero. An identification algorithm is presented and analysed. A recursive formulation is proposed, an extension for the identification of a non-linear time-series is also proposed.
Keywords : Binary data AR time-series Autoregressive models One-bit quantised observation sequences Identification algorithm Time series quantisation (signal) Autoregressive processes Nonlinear time-series Quantisation
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02323954
Contributor : Mathieu Pouliquen Connect in order to contact the contributor
Submitted on : Monday, October 21, 2019 - 6:20:04 PM
Last modification on : Thursday, March 24, 2022 - 2:14:08 PM

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COMUE-NORMANDIE | ENSICAEN | UNICAEN | LAC | LAC-IDO | TDS-MACS

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Romain Auber, Mathieu Pouliquen, Eric Pigeon, Olivier Gehan, Mohammed M'Saad, et al.. Identification of AR Time-series based on binary data. IET Signal Processing, Institution of Engineering and Technology, 2019, 14 (1), pp.24-31. ⟨10.1049/iet-spr.2019.0152⟩. ⟨hal-02323954⟩

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