autocovariance वाक्य
उदाहरण वाक्य
मोबाइल
- In some fields, the term is used interchangeably with autocovariance.
- This implies that the autocovariance is decaying to 0 sufficiently quickly.
- The integral is over the equilibrium flux autocovariance function.
- Some authors refer to R as the autocovariance function.
- The decay of the autocovariance function is power-like and so is slower than exponential.
- At zero time the autocovariance is positive since it is the mean square value of the flux at equilibrium.
- Either the autocovariance drops to zero after a certain time-lag, or it eventually has an exponential decay.
- One way of characterising long-range and short-range dependent stationary process is in terms of their autocovariance functions.
- WSS random processes only require that 1st moment ( i . e . the mean ) and autocovariance do not vary with respect to time.
- Where q is some maximum lag over which short-range autocorrelation might be substantial and C ( j ) is the sample autocovariance at lag j.
- When the autocorrelation function is normalized by mean and variance, it is sometimes referred to as the "'autocorrelation coefficient "'. or autocovariance function.
- The autocorrelation function, more properly called the autocovariance function unless it is normalized in some appropriate fashion, measures the strength of the correlation between the values of separated by a time lag.
- It is common practice in some disciplines, other than statistics and time series analysis, to drop the normalization by " ? 2 " and use the term " autocorrelation " interchangeably with " autocovariance ".
- Here \ gamma _ m is the autocovariance function of X t, \ sigma _ \ varepsilon is the standard deviation of the input noise process, and \ delta _ { m, 0 } is the Kronecker delta function.
- Where m = 0, \ ldots, p, yielding p + 1 equations . \ gamma _ m is the autocovariance function of X, \ sigma _ \ varepsilon is the standard deviation of the input noise process, and \ delta _ m is the Kronecker delta function.
- It can be seen that the autocovariance function decays with a decay time ( also called time constant ) of \ tau =-1 / \ ln ( \ varphi ) [ to see this, write B _ n = K \ varphi ^ { | n | } where K is independent of n.
- One way of making the PCA less arbitrary is to use variables scaled so as to have unit variance, by standardizing the data and hence use the autocorrelation matrix instead of the autocovariance matrix as a basis for PCA . However, this compresses ( or expands ) the fluctuations in all dimensions of the signal space to unit variance.
- The above two ways are mathematically related to each other, but they are not the only ways to define LRD . In the case where the autocovariance of the process does not exist ( heavy tails ), one has to find other ways to define what LRD means, and this is often done with the help of self-similar processes.
- By the positive definiteness of the autocovariance function, it follows from Bochner's theorem that there exists a positive measure " ? " on the real line such that " H " is isomorphic to the Hilbert subspace of " L " 2 ( " ? " ) generated by { " e " 2?i??" t " }.
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