stationary process

In the mathematical sciences, a stationary process (or strict(ly) stationary process) is a stochastic process in which the probability density function of some random variable X does not change over time or position. As a result, parameters such as the mean and variance also do not change over time or position. As an example, the measurement of white noise is stationary. Alternatively, the measurement of a cymbal clashing is not stationary. Although a cymbal clash is basically white noise, the measurement of that noise varies over time: Before the clash, there is silence, and after the clash, the noise gradually diminishes. Stationarity is used as a tool in time series analysis, where the raw data are often transformed to become stationary, for example, economic data are often seasonal and/or dependent on the price level. Processes are described as trend stationary if they are a linear combination of a stationary process and one or more processes exhibiting a trend. Transforming this data to leave a stationary data set for analysis is referred to as de-trending.

Weak or wide-sense stationarity

A weaker form of stationarity commonly employed in signal processing is known as weak-sense, wide-sense stationarity (WSS), second-order stationarity or covariance stationarity. WSS random processes only require that 1st and 2nd moments do not vary with respect to time. So, a continuous-time random process x(t) which is WSS has the following restrictions on its mean function

\mathbb{E}\{x(t)\} = m_x(t) = m_x(t + \tau) \,\, \forall \, \tau \in \mathbb{R}

and correlation function

\mathbb{E}\{x(t_1)x(t_2)\} = R_x(t_1, t_2) = R_x(t_1 + \tau, t_2 + \tau) = R_x(t_1 - t_2, 0) \,\, \forall \, \tau \in \mathbb{R}

The first property implies that the mean function m_x(t) must be constant. The second property implies that the correlation function depends only on the difference between

t_1

and

t_2

and only needs to be indexed by one variable rather than two variables. Thus, instead of writing,

\,\!R_x(t_1 - t_2, 0)

we usually abbreviate the notation and write

R_x(\tau) \,\! \mbox{ where } \tau = t_1 - t_2

When processing WSS random signals with linear, time-invariant (LTI) filters, it is helpful to think of the correlation function as a linear operator. Since it is a circulant operator (depends only on the difference between the two arguments), its eigenfunctions are the Fourier complex exponentials. Additionally, since the eigenfunctions of LTI operators are also complex exponentials, LTI processing of WSS random signals is highly tractable --- all computations can be performed in the frequency domain. Thus, the WSS assumption is widely employed in signal processing algorithms.

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