dlmodeler.build.arima: Build an ARIMA model

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Description

Builds an univariate ARIMA DLM of the specified order and coefficients.

Usage

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dlmodeler.arima(ar=c(), ma=c(), d=0,
                sigmaH = NA, sigmaQ = 0,
                name = "arima")

dlmodeler.build.arima(ar=c(), ma=c(), d=0,
                      sigmaH = NA, sigmaQ = 0,
                      name = "arima")

Arguments

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.

Details

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).

Value

An object of class dlmodeler representing the ARIMA model.

Note

State representations are not unique, so other forms could be used to achieve the same goals.

Currently, only ARMA models (d=0) are implemented.

Author(s)

Cyrille Szymanski <cnszym@gmail.com>

References

Durbin, and Koopman, Time Series Analysis by State Space Methods, Oxford University Press (2001), pages 46-48.

See Also

dlmodeler, dlmodeler.build, dlmodeler.build.polynomial, dlmodeler.build.dseasonal, dlmodeler.build.tseasonal, dlmodeler.build.structural, dlmodeler.build.regression

Examples

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# Example TODO

dlmodeler documentation built on May 29, 2017, 11:33 a.m.