Returns ets model applied to `y`

.

ets(y, model = "ZZZ", damped = NULL, alpha = NULL, beta = NULL, gamma = NULL, phi = NULL, additive.only = FALSE, lambda = NULL, biasadj = FALSE, lower = c(rep(1e-04, 3), 0.8), upper = c(rep(0.9999, 3), 0.98), opt.crit = c("lik", "amse", "mse", "sigma", "mae"), nmse = 3, bounds = c("both", "usual", "admissible"), ic = c("aicc", "aic", "bic"), restrict = TRUE, allow.multiplicative.trend = FALSE, use.initial.values = FALSE, ...)

y | a numeric vector or time series of class |
---|---|

model | Usually a three-character string identifying method using the framework terminology of Hyndman et al. (2002) and Hyndman et al. (2008). The first letter denotes the error type ("A", "M" or "Z"); the second letter denotes the trend type ("N","A","M" or "Z"); and the third letter denotes the season type ("N","A","M" or "Z"). In all cases, "N"=none, "A"=additive, "M"=multiplicative and "Z"=automatically selected. So, for example, "ANN" is simple exponential smoothing with additive errors, "MAM" is multiplicative Holt-Winters' method with multiplicative errors, and so on. It is also possible for the model to be of class |

damped | If TRUE, use a damped trend (either additive or
multiplicative). If NULL, both damped and non-damped trends will be tried
and the best model (according to the information criterion |

alpha | Value of alpha. If NULL, it is estimated. |

beta | Value of beta. If NULL, it is estimated. |

gamma | Value of gamma. If NULL, it is estimated. |

phi | Value of phi. If NULL, it is estimated. |

additive.only | If TRUE, will only consider additive models. Default is FALSE. |

lambda | Box-Cox transformation parameter. Ignored if NULL. Otherwise,
data transformed before model is estimated. When |

biasadj | Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities. |

lower | Lower bounds for the parameters (alpha, beta, gamma, phi) |

upper | Upper bounds for the parameters (alpha, beta, gamma, phi) |

opt.crit | Optimization criterion. One of "mse" (Mean Square Error),
"amse" (Average MSE over first |

nmse | Number of steps for average multistep MSE (1<= |

bounds | Type of parameter space to impose: |

ic | Information criterion to be used in model selection. |

restrict | If |

allow.multiplicative.trend | If |

use.initial.values | If |

... | Other undocumented arguments. |

An object of class "`ets`

".

The generic accessor functions `fitted.values`

and `residuals`

extract useful features of the value returned by `ets`

and associated
functions.

Based on the classification of methods as described in Hyndman et al (2008).

The methodology is fully automatic. The only required argument for ets is the time series. The model is chosen automatically if not specified. This methodology performed extremely well on the M3-competition data. (See Hyndman, et al, 2002, below.)

Hyndman, R.J., Koehler, A.B., Snyder, R.D., and Grose, S. (2002)
"A state space framework for automatic forecasting using exponential
smoothing methods", *International J. Forecasting*, **18**(3),
439--454.

Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The admissible
parameter space for exponential smoothing models". *Annals of
Statistical Mathematics*, **60**(2), 407--426.

Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008)
*Forecasting with exponential smoothing: the state space approach*,
Springer-Verlag. http://www.exponentialsmoothing.net.

`HoltWinters`

, `rwf`

,
`Arima`

.