A generalisation of conventional linear prediction (LP) is proposed. The method implements the prediction using a parallel structure, where the input is first filtered with two FIRs having zeros at z=1 and z=2. Two symmetric LP polynomials are then defined from the pre-filter outputs. The generalised LP inverse filter is obtained by convolving the symmetric polynomials with fixed pre-filters with zeros at z=±1 and then summing the obtained polynomials. It is shown that by selecting -12<1+1, the resulting all-pole filter is stable. Conventional LP is obtained by choosing 1=1. 0 and 2=-1.0
The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the ...
In this paper, linear prediction of signals is realized with an adaptive filter structure using a cl...
Abstract. In most machine learning applications the time series to predict is fixed and one has to l...
The autocorrelation and covariance methods of linear prediction axe formulated in terms of an invers...
AbstractA new inverse factorization technique is presented for solving linear prediction problems ar...
The autocorrelation and covariance methods of linear prediction are formulated in terms of an invers...
[[abstract]]This criterion requires only partial Mth-order cumulants CM,e(0,k1, k1, . . ., kM/2-1, k...
Linear prediction theory has had a profound impact in the field of digital signal processing. Althou...
We present an introduction to some aspects of digital signal processing and time series analysis whi...
International audienceThis paper derives an optimal linear-predictor of ARMA type in lattice form of...
In the article the autor discusses prediction problems in the general linear model where disturbance...
This paper considers general, pure linear prediction schemes, where the prediction of the input sign...
A modified autocorrelation method of linear prediction is proposed for pitch-synchronous analysis of...
New methods for the design, analysis and generalization of polynomial predictive filters (polynomial...
The analysis of an observed univariate time series is often undertaken in order to get a prediction ...
The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the ...
In this paper, linear prediction of signals is realized with an adaptive filter structure using a cl...
Abstract. In most machine learning applications the time series to predict is fixed and one has to l...
The autocorrelation and covariance methods of linear prediction axe formulated in terms of an invers...
AbstractA new inverse factorization technique is presented for solving linear prediction problems ar...
The autocorrelation and covariance methods of linear prediction are formulated in terms of an invers...
[[abstract]]This criterion requires only partial Mth-order cumulants CM,e(0,k1, k1, . . ., kM/2-1, k...
Linear prediction theory has had a profound impact in the field of digital signal processing. Althou...
We present an introduction to some aspects of digital signal processing and time series analysis whi...
International audienceThis paper derives an optimal linear-predictor of ARMA type in lattice form of...
In the article the autor discusses prediction problems in the general linear model where disturbance...
This paper considers general, pure linear prediction schemes, where the prediction of the input sign...
A modified autocorrelation method of linear prediction is proposed for pitch-synchronous analysis of...
New methods for the design, analysis and generalization of polynomial predictive filters (polynomial...
The analysis of an observed univariate time series is often undertaken in order to get a prediction ...
The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the ...
In this paper, linear prediction of signals is realized with an adaptive filter structure using a cl...
Abstract. In most machine learning applications the time series to predict is fixed and one has to l...