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 ?
Looking at Figures12 and 13, terms of ACF and PACF graphs for the first-difference time series were within confidence limits and thus they produced stationary time series.
It covers stationary time series, linear filters, ARMA models, other stationary time series models, nonstationary time series models, forecasting, parameter estimation, model identification, model building, vector-valued time series, long-memory processes, wavelets, and g-stationary processes.
According to investigation, Granger Causality exists for some stationary time series.
The stationary time series data have temporary shocks which disappear over the time and series move back to their long-run means values.
The first thing to note is that most of time series are non-stationary, and the AR and MA aspects of an ARIMA model refers only to a stationary time series.
The logarithmic growth rate of carbon emission and GDP are stationary time series.
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.
If the time series do not fulfill a weak stationarity condition, the stationary time series will be defined due to first differences of the relevant variables.
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.
This model explains the observed upward trend as extra variability due to persistence in a stationary time series.