scholarly journals Linear prediction error methods for stochastic nonlinear models

Automatica ◽  
2019 ◽  
Vol 105 ◽  
pp. 49-63 ◽  
Author(s):  
Mohamed Rasheed-Hilmy Abdalmoaty ◽  
Håkan Hjalmarsson
Keyword(s):  
Prediction Error ◽  
Linear Prediction ◽  
Nonlinear Models ◽  
Prediction Error Methods

Estimating Optimum Filter Length in Linear Prediction

1997 ◽  
Vol 51 (5) ◽  
pp. 718-720 ◽  
Author(s):  
O.-P. Sievänen
Keyword(s):  
Raman Spectra ◽  
Prediction Error ◽  
Linear Prediction ◽  
Signal To Noise Ratio ◽  
New Method ◽  
Signal To Noise ◽  
Filter Length ◽  
Optimum Filter ◽  
Good Signal ◽  
Prediction Filter

In this article a new method to estimate optimum filter length in linear prediction is described. Linear prediction was used to enhance resolution of a spectrum. In particular, the dependence of prediction error on filter length has been studied. With calculations of simulated spectra it is shown that the prediction error falls rapidly when the filter length attains its optimum value. This effect is quite pronounced when the spectrum has a good signal-to-noise ratio and the modified covariance method is used to calculate prediction filter coefficients. The method is illustrated with applications to real Raman spectra.

Prediction error methods in learning jump ARMAX models

2018 ◽  
Author(s):  
Valentine Breschi ◽  
Alberto Bemporad ◽  
Dario Piga ◽  
Stephen Boyd
Keyword(s):  
Prediction Error ◽  
Prediction Error Methods

scholarly journals Short-term prediction of rain attenuation level and volatility in Earth-to-Satellite links at EHF band

2008 ◽  
Vol 15 (4) ◽  
pp. 631-643 ◽  
Author(s):  
L. de Montera ◽  
C. Mallet ◽  
L. Barthès ◽  
P. Golé
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Nonlinear Models ◽  
Garch Model ◽  
Rain Attenuation ◽  
Frequency Scaling ◽  
Attenuation Level ◽  
Satellite Links ◽  
Short Term Prediction ◽  
The Cost

Abstract. This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20–50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.

scholarly journals Identification of dynamic models in complex networks with prediction error methods—Basic methods for consistent module estimates

Automatica ◽  
2013 ◽  
Vol 49 (10) ◽  
pp. 2994-3006 ◽  
Author(s):  
Paul M.J. Van den Hof ◽  
Arne Dankers ◽  
Peter S.C. Heuberger ◽  
Xavier Bombois
Keyword(s):  
Complex Networks ◽  
Prediction Error ◽  
Dynamic Models ◽  
Prediction Error Methods
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