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Time series analysis: recursive methods and their modifications for time series with outliers and missing observations.

Tomás Cipra, Asunción Rubio, José Trujillo (1991)

Extracta Mathematicae

The recursive methods are popular in time series analysis since they are computationally efficient and flexible enough to treat various changes in character of data. This paper gives a survey of the most important type of these methods including their classification and relationships existing among them. Special attention is devoted to i) robustification of some recursive methods, capable of facing outliers in time series, and ii) modifications of recursive methods for time series with missing observations....

Time series model identification by estimating information, memory and quantiles.

Emanuel Parzen (1983)

Qüestiió

This paper applies techniques of Quantile Data Analysis to non-parametrically analyze time series functions such as the sample spectral density, sample correlations and sample partial correlations. The aim is to identify the memory type of an observed time series, and thus to identify parametric time domain models that fit an observed time series. Time series models are usually tested for adequacy by testing if their residuals are white noise. It is proposed that an additional criterion of fit for...

Time series models for Earth's crust kinematics

Magda Komorníková, Jozef Komorník (2002)

Kybernetika

Deterministic and stochastic approach to modeling common trends has been applied to time series of horizontal coordinates of the permanent GPS station Modra – Piesky (recorded weekly during the period of 4 years).

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