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

Abstract : 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.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-01826047
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Submitted on : Thursday, June 28, 2018 - 10:55:18 PM
Last modification on : Saturday, June 25, 2022 - 9:51:58 AM

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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⟩

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