Estimation of Auto-Regressive models for time series using Binary or Quantized Data

Romain Auber (1, 2) , Mathieu Pouliquen (1) , Eric Pigeon (1) , Mohammed M'Saad (1) , Olivier Gehan (1) , Pierre Alexandre Chapon (2) , Sébastien Moussay (3, 2)

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

In this paper, we first present an algorithm for the estimation of an Auto-Regressive model of time series using output data of a binary sensor. This algorithm is based on the estimation of the autocorrelation of time series for a threshold different from zero. The algorithm is then extended to time series with several quantization levels. Simulation results are given to show the effectiveness of the proposed approaches.

Mots clés: Auto-Regressive model; Time series; Binary Sensor; Quantized Data; Parameter estimation algorithm

Domaines: Automatique / Robotique

Romain Auber, Mathieu Pouliquen, Eric Pigeon, Mohammed M'Saad, Olivier Gehan, et al.. Estimation of Auto-Regressive models for time series using Binary or Quantized Data. IFAC Symposium on System Identification, Jul 2018, Stockholm, Sweden. pp.581-586, ⟨10.1016/j.ifacol.2018.09.221⟩. ⟨hal-01826047⟩