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In addition, there needs to be a run of stable, non-trended (stationary) data values to obtain accurate control limits.
This is an encouragement to continue this work and to test other techniques suitable for stationary data.
This concept is based on inherently stationary data and validated with field data that are collected from the stationary boundary layer (SBL).
In this regard, it is possible to record and store relatively long wave data sequences, whereas the commonly adopted in-practice assumption of stationary data is abandoned.
Though originally designed for stationary data, we have noticed in simulations the ability of the CMA to achieve the separation even in non-stationary contexts such as mixtures of linearly modulated communication signals having arbitrary symbol periods.
A number of results appear in the literature that deal with the case where the stationary data is given as the output of a linear system driven by Gaussian noise.
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We will cover variogram estimation and fitting, kriging, fixed-and increasing-domain asymptotics, as well as methods for modeling non-stationary data.
With IL, the system self-adjusts to accommodate new and possibly non-stationary data samples arriving over time.
While the influence of growth is relatively modest, fitting non-stationary data to a stationary curve gives erroneous results.
In this contribution, two different approaches, for merging operational mode shapes from non-stationary data, are proposed.
Thus the proposed method can be considered as a better classifier for healthy and medical problems and also when the great non-stationary data should be classified.
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