This paper presents an overview of the main methods used to identify autoregressive
models with additive noises. The classification of identification methods is given. For each …

This paper proposes a new recurrent neural network-based Kalman filter for speech
enhancement, based on a noise-constrained least squares estimate. The parameters of …

In signal processing, ARMA processes are widely used to model short-memory processes.
In various applications, comparing or classifying ARMA processes is required. In this paper …

This paper considers the problem of determining linear relations from data affected by
additive noise in the context of the Frisch scheme. The loci of solutions of the Frisch scheme …

We address the issue of detecting hidden periodicity when the signal exhibits periodic
correlation, but is additionally affected by non-Gaussian noise with unknown characteristics …

Thermal sensor noise has a great impact on the efficiency and effectiveness of a dynamic
thermal management (DTM) strategy. To address the problem of forecasting temperature …

This study deals with the estimation of a vector process disturbed by an additive white noise.
When this process is modelled by a multivariate autoregressive (M-AR) process, optimal …

In this paper we present a new method for estimating the parameters of an autoregressive
(AR) signal from observations corrupted with white noise. The least-squares (LS) estimate of …

Time series models play a key-role in many applications from biomedical signal analysis to
applied econometric. The purpose of this paper is to compare 1 st-order moving-average …

Heart rate variability (HRV) was largely used to evaluate psychophysiological status of
Human at rest as well as during cognitive tasks, for both healthy subjects and patients …