Description Usage Arguments Details Value Author(s) References See Also Examples
naive is the model constructor for a random walk model applied to y
.
This is equivalent to an ARIMA(0,1,0) model. naive()
is simply a wrapper
to maintain forecast package similitude. seasonal
returns the model constructor
for a seasonal random walk equivalent to an ARIMA(0,0,0)(0,1,0)m model where m is the
seasonal period.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
ts |
a numeric or ts object with the univariate time series. |
seasonal |
a Boolean value for select a seasonal random walk instead. |
m |
an optional integer value for the seasonal period. |
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 |
series.name |
an optional string vector with the series names. |
... |
Further arguments passed to |
The random walk with drift model is
Y[t]= mu_0 +Y[t-1] + epsilon[t]
where epsilon[t] is a normal iid error.
The seasonal naive model is
Y[t]= mu_0 +Y[t-m] + epsilon[t]
where epsilon[t] is a normal iid error.
A varstan
object with the fitted naive Random Walk model.
Asael Alonzo Matamoros
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
.
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
.
1 2 3 | library(astsa)
# A seasonal Random-walk model.
sf1 = stan_naive(birth,seasonal = TRUE,iter = 500,chains = 1)
|
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