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Spectral estimation by the model of Autoregressive Moving Average and its resolution power

Ігор Прокопенко, A. J. Churina

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Abstract

This work addresses the problem of stochastic modelling of short time series. Questions of autoregressive spectral analysis and autoregressive moving average spectral analysis and its application in the tasks of signal processing of informational measuring systems are considered. Autoregressive and autoregressive moving average spectral methods as well as by discrete Fourier transform are given.

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What this paper is about

This work addresses the problem of stochastic modelling of short time series. Questions of autoregressive spectral analysis and autoregressive moving average spectral analysis and its application in the tasks of signal processing of informational measuring systems are considered. Autoregressive and autoregressive moving average spectral methods as well as by discrete Fourier transform are given.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This work addresses the problem of stochastic modelling of short time series. Questions of autoregressive spectral analysis and autoregressive moving average spectral analysis and its application in the tasks of signal processing of informational measuring systems are considered. Autoregressive and autoregressive moving average spectral methods as well as by discrete Fourier transform are given.

Key concepts: Autoregressive model, STAR model, Spectral density estimation, Nonlinear autoregressive exogenous model, Autoregressive–moving-average model, Spectral density, SETAR, Autoregressive integrated moving average

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