Description Usage Arguments Details Value Author(s) References See Also Examples
Fitting a SARIMA model in Stan.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | stan_sarima(
ts,
order = c(1, 0, 0),
seasonal = c(0, 0, 0),
xreg = NULL,
period = 0,
chains = 4,
iter = 2000,
warmup = floor(iter/2),
adapt.delta = 0.9,
tree.depth = 10,
stepwise = TRUE,
prior_mu0 = NULL,
prior_sigma0 = NULL,
prior_ar = NULL,
prior_ma = NULL,
prior_sar = NULL,
prior_sma = NULL,
prior_breg = NULL,
series.name = NULL,
...
)
|
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. |
chains |
An integer of the number of Markov Chains chains to be run, by default 4 chains are run. |
iter |
An integer of total iterations per chain including the warm-up, by default the number of iterations are 2000. |
warmup |
A positive integer specifying number of warm-up (aka burn-in)
iterations. This also specifies the number of iterations used for step-size
adaptation, so warm-up samples should not be used for inference. The number
of warmup should not be larger than |
adapt.delta |
An optional real value between 0 and 1, the thin of the jumps in a HMC method. By default is 0.9. |
tree.depth |
An integer of the maximum depth of the trees evaluated during each iteration. By default is 10. |
stepwise |
If TRUE, will do stepwise selection (faster). Otherwise, it searches over all models. Non-stepwise selection can be very slow, especially for seasonal models. |
prior_mu0 |
The prior distribution for the location parameter in an ARIMA model. By default
the value is set |
prior_sigma0 |
The prior distribution for the scale parameter in an ARIMA model. By default
the value is set |
prior_ar |
The prior distribution for the auto-regressive parameters in an ARIMA model.
By default the value is set |
prior_ma |
The prior distribution for the moving average parameters in an ARIMA model.
By default the value is set |
prior_sar |
The prior distribution for the seasonal auto-regressive parameters in a
SARIMA model. By default the value is set |
prior_sma |
The prior distribution for the seasonal moving average parameters in a
SARIMA model. By default the value is set |
prior_breg |
The prior distribution for the regression coefficient parameters in a
ARIMAX model. By default the value is set |
series.name |
an optional string vector with the series names. |
... |
Further arguments passed to |
The function returns a varstan
object with the fitted model.
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)
A varstan
object with the fitted SARIMA model.
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
1 2 3 4 5 6 7 8 9 | library(astsa)
# Declare a multiplicative seasonal ARIMA model for the birth data.
sf1 = stan_sarima(birth,order = c(0,1,2),
seasonal = c(1,1,1),iter = 500,chains = 1)
#Declare an Dynamic Harmonic Regression model for the birth data.
sf2 = stan_sarima(birth,order = c(1,0,1),
xreg = fourier(birth,K = 2),iter = 500,chains = 1)
|
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