The question of minimizing the bias due to the survey sampling error when estimating the autocorrelation function of aggregated AR(1) processes is studied.

An algorithm is given for computing the serial correlations of the waiting time, and of the time in system, for successive customers in a GI/G/1 queue. The method depends on representing the inter-arrival time distribution in terms of a process in class K_r (i.e., distributions with a rational...

We study correlation and spectral properties of chaotic self-sustained oscillations of different types. It is shown that some classical models of stochastic processes can be used to describe behavior of autocorrelation functions of chaos. The influence of noise on chaotic systems is also considered.

The seasonal dependency or seasonality is a general component of the time series pattern that can be examined via correlograms, where the correlogram displays graphical and numerical information in an autocorrelation function. This paper discusses the use of an autocorrelation function in the...

The availability of hourly wind speed data is becoming increasingly important for ensuring the proper design of wind energy conversion systems. For many sites, measured series of such high resolution are incomplete or entirely lacking; hence the need for a model for synthesizing wind speed data.

An economic time series can often be viewed as a noisy proxy for an underlying economic variable. Measurement errors will influence the dynamic properties of the observed process and may conceal the persistence of the underlying time series. In this paper we develop instrumental variable (IV)...

The paper „Stationary and Non-stationary Time Series” presents in a theoretical approach, the concept of time series, its characteristics which are: variability, homogeneity, periodicity and interdependence of time series terms, from which result the methods of estimation and analysis of...

A maximum likelihood estimation method implemented in S-PLUS (S-MLE) to estimate the Hurst coefficient (H) is evaluated. The Hurst coefficient, with 0.5H1, characterizes long memory time series by quantifying the rate of decay of the autocorrelation function. S-MLE was developed to estimate H...