fit.tfm | R Documentation |

`fit`

fits the univariate model to the time series z.

## S3 method for class 'tfm' fit( mdl, y = NULL, method = c("exact", "cond"), optim.method = "BFGS", show.iter = FALSE, fit.noise = TRUE, envir = NULL, ... ) fit(mdl, ...) ## S3 method for class 'um' fit( mdl, z = NULL, method = c("exact", "cond"), optim.method = "BFGS", show.iter = FALSE, envir = NULL, ... )

`mdl` |
an object of class |

`y` |
a |

`method` |
Exact/conditional maximum likelihood. |

`optim.method` |
the |

`show.iter` |
logical value to show or hide the estimates at the different iterations. |

`fit.noise` |
logical. If TRUE parameters of the noise model are fixed. |

`envir` |
environment in which the function arguments are evaluated. If NULL the calling environment of this function will be used. |

`...` |
additional arguments. |

`z` |
a time series. |

A `tfm`

object.

An object of class "um" with the estimated parameters.

The `um`

function estimates the corresponding ARIMA model when a time
series is provided. The `fit`

function is useful to fit a model to
several time series, for example, in a Monte Carlo study.

z <- AirPassengers airl <- um(i = list(1, c(1, 12)), ma = list(1, c(1, 12)), bc = TRUE) airl <- fit(airl, z)

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