bayesforecast (version 1.0.5)

stan_naive: Naive and Random Walk models.

Description

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.

Usage

stan_naive(
  ts,
  seasonal = FALSE,
  m = 0,
  chains = 4,
  iter = 2000,
  warmup = floor(iter/2),
  adapt.delta = 0.9,
  tree.depth = 10,
  prior_mu0 = NULL,
  prior_sigma0 = NULL,
  series.name = NULL,
  ...
)

Value

A varstan object with the fitted naive Random Walk model.

Arguments

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, chains = 4.

iter

an integer of total iterations per chain including the warm-up. By default, iter = 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 warm-up iteration should not be larger than iter.By default, warmup = iter/2.

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.

prior_mu0

The prior distribution for the location parameter in an ARIMA model. By default, sets student(7,0,1) prior.

prior_sigma0

The prior distribution for the scale parameter in an ARIMA model. By default, declares a student(7,0,1) prior.

series.name

an optional string vector with the series names.

...

Further arguments passed to varstan function.

Author

Asael Alonzo Matamoros

Details

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.

References

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.

See Also

Sarima.

Examples

# \donttest{
 # A seasonal Random-walk model.
 sf1 = stan_naive(birth,seasonal = TRUE,iter = 500,chains = 1)
# }