Description Usage Arguments Details Value Note Author(s) References See Also Examples
Builds an univariate ARIMA DLM of the specified order and coefficients.
1 2 3 4 5 6 7 |
ar |
vector of autoregressive coefficients c(ar1, ar2, ar3...). |
ma |
vector of moving average coefficients c(ma1, ma2, ma3...). |
d |
order of differenciation. |
sigmaH |
std dev of the observation disturbance (if unknown, set to NA and use dlmodeler.fit to estimate it). Default = NA. |
sigmaQ |
std dev of the state disturbances (if unknown, set to NA and use dlmodeler.fit to estimate it). Default = 0. |
name |
an optional name to be given to the resulting DLM. |
The autoregressive terms of the model are ar[1] + ar[2]L + ... ar[p]L^p where L is the lag operator.
The moving average terms of the model are 1 + ma[1]L + ... ma[q]L^q where L is the lag operator.
The initial value P0inf
is parametered to use exact diffuse initialisation
(if supported by the back-end).
An object of class dlmodeler
representing the ARIMA model.
State representations are not unique, so other forms could be used to achieve the same goals.
Currently, only ARMA models (d=0) are implemented.
Cyrille Szymanski <cnszym@gmail.com>
Durbin, and Koopman, Time Series Analysis by State Space Methods, Oxford University Press (2001), pages 46-48.
dlmodeler
,
dlmodeler.build
,
dlmodeler.build.polynomial
,
dlmodeler.build.dseasonal
,
dlmodeler.build.tseasonal
,
dlmodeler.build.structural
,
dlmodeler.build.regression
1 | # Example TODO
|
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