Those old enough can remember when talking on the telephone, which was stationary, meant sitting down, putting your feet up and chatting — not doing laundry, cooking dinner, sweeping the floor and answering the door.
For (overline{g}<1), the vector of rates (r_{[n]}{(t)}) is a linearly filtered second-order stationary random process and therefore approaches a stationary mean and variance.
At present, the dynamic characteristic evaluation of pantograph catenary system is mainly based on statistical parameters, such as the stationary mean value and variance in the European standard [2].
We show in Appendix 4 that, after sufficient time, (y=rho) assumes a stationary distribution with mean (mu{({{x}}, g)} = deltatau _{d} gphi + {{x}})) and variance (nu{({{x}}, g)} = delta^{2}tau_{d} (Cg^{2} + frac{gphi+{{x}}}{2} )), where ϕ is the stationary mean of (I{(t)}), and C is a positive constant determined by the stationary autocovariance of (I{(t)}).
When (X_{t}) is stationary (mean (EX_{t}=0) assumed), the lag k autocorrelation of a calendar month subseries is the lag kq, or k-th seasonal autocorrelation, of (X_{t}).
We consider the rate model described by the differential equation tau_{r} dot{r} = -r +gI{(t)} +{{x}}, (15) where (I{(t)}) is any second-order stationary process, that is, a process with stationary mean (phi= langle I{(t)}rangle) and stationary autocovariance function (R(w) = langle I{(t)} I{(t+w)} rangle- phi^{2}), both independent of t.
The most corroborated Constant Temperature Equivalent (CTE) model measures the proportion of development occurring above the threshold temperature, and predicts that fluctuations with constant variance about a stationary mean produce equal sex ratios as a constant temperature (i.e. CTE value) [6].
The use of STOK, as with OK, implies the adoption of a RF model with stationary mean and variogram.
Both spatial-only and space time geostatistical prediction techniques generally rely on the fitting of a random function (RF) model parameterised with a stationary mean and variogram.
This ACF is typical of stochastic time series fluctuating around a stationary mean: it decreases very rapidly with the autocorrelation delay and is roughly devoid of subsequent oscillations.
