Description Usage Arguments Details Value Author(s) References See Also Examples
Constructor of the SARIMA model for Bayesian estimation in Stan.
1 |
ts |
a numeric or ts object with the univariate time series. |
order |
A specification of the non-seasonal part of the ARIMA model: the three components (p, d, q) are the AR order, the number of differences, and the MA order. |
seasonal |
A specification of the seasonal part of the ARIMA model,same as order parameter: the three components (p, d, q) are the seasonal AR order, the degree of seasonal differences, and the seasonal MA order. |
xreg |
Optionally, a numerical matrix of external regressors, which must have the same number of rows as ts. It should not be a data frame. |
period |
an integer specifying the periodicity of the time series by default the value frequency(ts) is used. |
series.name |
an optional string vector with the series names. |
The function returns a list with the data for running stan()
function of
rstan package
If xreg
option is used, the model by default will cancel the
seasonal differences adjusted (D = 0). If a value d
> 0 is used, all
the regressor variables in xreg
will be difference as well.
The default priors used in Sarima are:
ar ~ normal(0,0.5)
ma ~ normal(0,0.5)
mu0 ~ t-student(0,2.5,6)
sigma0 ~ t-student(0,1,7)
sar ~ normal(0,0.5)
sma ~ normal(0,0.5)
breg ~ t-student(0,2.5,6)
For changing the default prior use the function set_prior
The function returns a list with the data for running stan()
function of
rstan package.
Asael Alonzo Matamoros
Box, G. E. P. and Jenkins, G.M. (1978). Time series analysis: Forecasting and
control. San Francisco: Holden-Day. Biometrika, 60(2), 297-303.
doi:10.1093/biomet/65.2.297
.
Kennedy, P. (1992). Forecasting with dynamic regression models: Alan Pankratz, 1991.
International Journal of Forecasting. 8(4), 647-648.
url: https://EconPapers.repec.org/RePEc:eee:intfor:v:8:y:1992:i:4:p:647-648
.
Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the
forecast package for R
. Journal of Statistical Software. 26(3),
1-22.doi: 10.18637/jss.v027.i03
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