| Package | Description |
|---|---|
| dev.nm.stat.timeseries.linear.multivariate | |
| dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma |
| Modifier and Type | Class and Description |
|---|---|
class |
MultivariateAutoCorrelationFunction
This is the auto-correlation function of a multi-dimensional time series {Xt}.
\[
Y_{i,j} = E((X_i - \mu_i) \times (X_j - \mu_j)')
\\
\rho(i, j) = \frac{Y_{i,j}}{\sqrt{Y_{i,i}Y_{j,j}}}
\]
For a stationary process, the auto-correlation depends only on the lag, |i - j|.
|
class |
MultivariateAutoCovarianceFunction
This is the auto-covariance function of a multi-dimensional time series \(\{X_{t}\}\),
\[
K(i, j) = E((X_i - \mu_i) \times (X_j - \mu_j)')
\]
For a stationary process, the auto-covariance depends only on the lag, |i - j|.
|
| Modifier and Type | Class and Description |
|---|---|
class |
VARMAAutoCorrelation
Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
\(EX_{t} = 0\).
|
class |
VARMAAutoCovariance
Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that
\(EX_{t} = 0\).
|
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