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Determine the model order m via Section 3.2.
For the AR estimator, the estimated model order M for the minimum error is presented.
With the calculated drops of singular, the system model order m can be selected as.
4) Determine the model order m via Section 3.2. 5) Reconstruct the WAMS covariance matrix C ′ with (16).
Conversely, if the sampling rate is too high it can be computationally intractable to model far enough into the future (i.e. high model order m) to capture interactions.
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where M is the model order, A m >0 and ϕ m ∈[0,2π] are the amplitude and phase of the m th harmonic, f0∈[ 0,π/m] is the fundamental frequency, and fs is the sampling frequency.
An autoregressive model of order m, generally denoted by AR (m) [3] has the form ϕ Ω X t = ε t (1).
An autoregressive moving average model of order (m, n), generally denoted by ARMA (m, n) [3] has the form ϕ Ω X t = θ Ω ε t. (2).
For example, let '1' and '0' represent the busy and idle states of a particular channel, a fixed-order Markov model of order m has strings of channel observations of the past m time slots as its state labels.
An autoregressive integrated moving average model of order (m, τ, n) which is generally denoted by ARIMA (m, τ, n) [3] has the form ϕ Ω Δ τ X t = θ Ω ε t. (3).
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