Open AccessIdentification of AR time‐series based on binary data
Author(s) -
Auber Romain,
Pouliquen Mathieu,
Pigeon Eric,
Gehan Olivier,
M'Saad Mohammed,
Chapon Pierre Alexandre,
Moussay Sebastien
Publication year - 2020
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2019.0152
Subject(s) - series (stratigraphy) , identification (biology) , time series , binary number , extension (predicate logic) , algorithm , autoregressive model , mathematics , computer science , statistics , arithmetic , paleontology , botany , biology , programming language
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.