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We then show that such moving average processes are equivalent to state space models with stochastic design components.
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Long-term variations of recorded runoff and its 5-year moving average process are shown in Fig. 4.
To a first approximation, simple, nonstationary time series models are introduced and standard techniques for determining the parameters of autoregressive integrated moving-average processes are applied.
In this technique signal synthesis [4] and signal averaging processes are incorporated to generate a desired impulsive signal from acoustic driver(s) and to eliminate background noise (flow noise) from in-duct and far field signals, respectively.
The moving average process was used as a matrix of time-based weights for analyzing the seasonal changes and autocorrelations.
This length of the moving average process was chosen to ensure optimal detrending.
Intrinsic and composite distribution functions, used in the averaging process, are defined and their basic properties are investigated.
This averaging process is especially important this year since the economic indicators are mixed.
This averaging process is repeated to find the best level for each of the 13 design variables.
Contrary to classical dual-media models, the averaging process is applied directly to Stokes problem and not to Darcy's laws.
A data averaging process is designed on the field servers to reduce the communication traffic and achieve consequential reliable data communication between the field servers and the BS in WSN.
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CEO of Professional Science Editing for Scientists @ prosciediting.com