bayesforecast (version 1.0.1)

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,
  stepwise = TRUE,
  prior_mu0 = NULL,
  prior_sigma0 = NULL,
  series.name = NULL,
  ...
)

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 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 iter and the default is 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.

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 NULL, then the default student(7,0,1) prior is used.

prior_sigma0

The prior distribution for the scale parameter in an ARIMA model. By default the value is set NULL, then the default student(7,0,1) prior is used.

series.name

an optional string vector with the series names.

...

Further arguments passed to varstan function.

Value

A varstan object with the fitted naive Random Walk model.

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

# NOT RUN {
 library(astsa)
 # A seasonal Random-walk model.
 sf1 = stan_naive(birth,seasonal = TRUE,iter = 500,chains = 1)
# }
# NOT RUN {
# }