Description Usage Arguments Value References Examples

Fits the best model from classes ARIMA, ETS, TBATS and NNETAR.

1 2 3 |

`x` |
A vector or ts object. |

`train` |
The (initial) percentage of the time series to be used to train the models. Must be |

`steps` |
Number of steps to forecast. If |

`max.points` |
Limits the maximum number of points to be used in modeling. Uses the first |

`show.main.graph` |
Logical. Should the main graphic (with the final model) be displayed? |

`show.sec.graph` |
Logical. Should the secondary graphics (with the training models) be displayed? |

`show.value` |
Logical. Should the values be displayed? |

`PI` |
Prediction Interval used in nnar models. May take long time processing. |

`theme.doj` |
Logical. Should the theme of Decades Of Jurimetrics be used? |

`$fcast`

Predicted time series using the model that minimizes the forecasting mean square error.

`$mse.pred`

Mean squared error of prediction. Used to decide the best model.

`$runtime`

Running time.

Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. otexts.com/fpp2.

https://robjhyndman.com/hyndsight/nnetar-prediction-intervals/

https://robjhyndman.com/talks/Google-Oct2015-part1.pdf

Zabala, F. J. and Silveira, F. F. (2019). Decades of Jurimetrics.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(jurimetrics)
fits(livestock)
fits(livestock, theme.doj = T)
fits(livestock, show.main.graph = F, show.sec.graph = T, show.value = F)
fits(h02, .9)
fits(gas)
data('tjmg_year')
y1 <- ts(tjmg_year$count, start = c(2000,1), frequency = 1)
fits(y1)
data(tjrs_year_month)
y2 <- ts(tjrs_year_month$count, start = c(2000,1), frequency = 12)
fits(y2, train = 0.8, steps = 24)
``` |

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