Combined ets-arima-theta forecasts

Combined ets, arima, and theta (eat) forecasting (uses forecast::ets, forecast::auto.arima, forecast::thetaf)

eatf(
  y,
  h = 5,
  level = 95,
  method = c("EAT", "E", "A", "T"),
  weights = rep(1/3, 3),
  type_pi = c("gaussian", "E", "A", "T"),
  ...
)

Arguments

y

a univariate time series

h

number of periods for forecasting

level

confidence level for prediction intervals

method

forecasting method: "E" for forecast::ets; "A"for forecast::auto.arima; "T" for forecast::thetaf; or "EAT" for the combination of the three (default, with weights)

weights

weights for each method, in method EAT. Must add up to 1.

type_pi

type of prediction interval: currently ETS: "E", Auto.Arima: "A" or Theta: "T"

...

additional parameters to be passed to forecast::ets, forecast::auto.arima, forecast::thetaf and forecast::forecast

Value

An object of class "forecast"; a list containing the following elements:

model

A list containing information about the fitted model

method

The name of the forecasting method as a character string

mean

Point forecasts for the time series

lower

Lower bound for prediction interval

upper

Upper bound for prediction interval

x

The original time series

residuals

Residuals from the fitted model

Details

ensemble forecasts obtained from forecast::ets, forecast::auto.arima and forecast::theta (with weights)

References

Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2021). forecast: Forecasting functions for time series and linear models. R package version 8.14, <URL: https://pkg.robjhyndman.com/forecast/>.

Hyndman RJ, Khandakar Y (2008). 'Automatic time series forecasting: the forecast package for R.' Journal of Statistical Software, 26 (3), 1-22. <URL: https://www.jstatsoft.org/article/view/v027i03>.

Assimakopoulos, V. and Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting 16, 521-530.

Hyndman, R.J., and Billah, B. (2003) Unmasking the Theta method. International J. Forecasting, 19, 287-290.

Author

T. Moudiki

Examples

require(forecast)

if (FALSE) {

print(ahead::eatf(WWWusage, method = "EAT",
weights = c(0.5, 0, 0.5)))

print(ahead::eatf(WWWusage, method = "EAT"))


obj <- ahead::eatf(WWWusage, method = "EAT",
weights = c(0, 0.5, 0.5), h=10,
type_pi = "T")
plot(obj)


obj <- ahead::eatf(WWWusage, method = "EAT",
weights = c(0, 0.5, 0.5), h=10, type_pi="A")
plot(obj)
}


par(mfrow=c(3, 2))
plot(ahead::eatf(USAccDeaths, h=10, level=95))
plot(ahead::eatf(AirPassengers, h=10, level=95, type_pi = "T"))
plot(ahead::eatf(lynx, h=10, level=95, type_pi = "A"))
plot(ahead::eatf(WWWusage, h=10, level=95, type_pi = "E"))
plot(ahead::eatf(Nile, h=10, level=95))
plot(ahead::eatf(fdeaths, h=10, level=95))