public class ARMAOutlierIdentificationEx3 extends Object
Forecasts an \(\text{ARMA}(2,1)\) time series contaminated by outliers.
This example is a realization of an \(\text{ARMA}(2,1)\) process described by the model \(Y_t-Y_{t-1}+0.24Y_{t-2} = 10.0+a_t+0.5a_{t-1},\, \{a_t\}\) a Gaussian White noise process. An additive outlier with \(\omega_1=4.5\) was added at time point \(t=150\), a temporary change outlier with \(\omega_2=3.0\) was added at time point \(t=200\).
Outliers were artificially added to the outlier free series \(\{Y-t\}_{t=1,\ldots, 280}\) at time points \(t=150\) (level shift with \(\omega_1=+2.5\)) and \(t=200\) (additive outlier with \(\omega_2=+3.2\)), resulting in the outlier contaminated series \(\{Z_t\}_{t=1,\ldots,280}\). For both series, forecasts were determined for time points \(t=281,\ldots,290\) and compared with the actual values of the series.
Constructor and Description |
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ARMAOutlierIdentificationEx3() |
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