stationary time series


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stationary time series

[′stā·shə‚ner·ē ′tīm ‚sir·ēz]
(statistics)
A time series which as a stochastic process is unchanged by a uniform increment in the time parameter defining it.
References in periodicals archive ?
Chapter 7, the first in the two-chapter sequence regarding time series, begins with the most basic time series models, namely, those univariate models employed when working with stationary time series data.
In our study, time series is stationary time series implying no change over time in variance, and autocorrelation structure.
After an introduction to stationarity and an analysis of stationary time series from both the time and frequency domains, the text covers topics such as linear filters, various stationary and non-stationary time series models, and wavelets.
The Box--Jenkins Methodology is valid for only stationary time series data.
That means that stationary time series are produced by means of the first or second differencing.
A stationary time series is significant to a regression analysis based on the time series, because useful information or characteristics are difficult to identify in a nonstationary time series.
Topics include pile-up probabilities for the Laplace likelihood estimator of a non-vertible first order moving average, prediction errors in regression models with non-stationary regressors, forecasting unstable processes, determining order in general vector autoregressions, conditional-sum-of-squares estimation of models for stationary time series with long memory, modeling macroeconomic time series via heavy tailed distributions, estimation errors in the Sharpe ratio for long-memory stochastic volatility, and multivariate volatility models.