naive: Naive and Random Walk models.

In bayesforecast: Bayesian Time Series Modeling with Stan

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

naive(ts, seasonal = FALSE, m = 0)

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.

Details

The random walk with drift model is

Y_t = mu_0 + Y_{t-1} + epsilon_t

where epsilon_t is a normal i.i.d. error.

The seasonal naive model is

Y_t = mu_0 + Y_{t-m} + epsilon_t

where epsilon_t is a normal i.i.d. error.

Value

The function returns a list with the data for running stan() function of rstan package.

Author(s)

Asael Alonzo Matamoros.

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

# A seasonal Random-walk model.
model = naive(birth,seasonal = TRUE)
model

bayesforecast documentation built on June 8, 2025, 10:42 a.m.