auto_adam_fit_impl | R Documentation |

Low-Level ADAM function for translating modeltime to forecast

```
auto_adam_fit_impl(
x,
y,
period = "auto",
p = 0,
d = 0,
q = 0,
P = 0,
D = 0,
Q = 0,
model = "ZXZ",
constant = FALSE,
regressors = c("use", "select", "adapt"),
outliers = c("ignore", "use", "select"),
level = 0.99,
occurrence = c("none", "auto", "fixed", "general", "odds-ratio", "inverse-odds-ratio",
"direct"),
distribution = c("default", "dnorm", "dlaplace", "ds", "dgnorm", "dlnorm", "dinvgauss",
"dgamma"),
loss = c("likelihood", "MSE", "MAE", "HAM", "LASSO", "RIDGE", "MSEh", "TMSE", "GTMSE",
"MSCE"),
ic = c("AICc", "AIC", "BIC", "BICc"),
select_order = FALSE,
...
)
```

`x` |
A data.frame of predictors |

`y` |
A vector with outcome |

`period` |
A seasonal frequency. Uses "auto" by default. A character phrase of "auto" or time-based phrase of "2 weeks" can be used if a date or date-time variable is provided. |

`p` |
The order of the non-seasonal auto-regressive (AR) terms. Often denoted "p" in pdq-notation. |

`d` |
The order of integration for non-seasonal differencing. Often denoted "d" in pdq-notation. |

`q` |
The order of the non-seasonal moving average (MA) terms. Often denoted "q" in pdq-notation. |

`P` |
The order of the seasonal auto-regressive (SAR) terms. Often denoted "P" in PDQ-notation. |

`D` |
The order of integration for seasonal differencing. Often denoted "D" in PDQ-notation. |

`Q` |
The order of the seasonal moving average (SMA) terms. Often denoted "Q" in PDQ-notation. |

`model` |
The type of ETS model. |

`constant` |
Logical, determining, whether the constant is needed in the model or not. |

`regressors` |
The variable defines what to do with the provided explanatory variables. |

`outliers` |
Defines what to do with outliers. |

`level` |
What confidence level to use for detection of outliers. |

`occurrence` |
The type of model used in probability estimation. |

`distribution` |
what density function to assume for the error term. |

`loss` |
The type of Loss Function used in optimization. |

`ic` |
The information criterion to use in the model selection / combination procedure. |

`select_order` |
If TRUE, then the function will select the most appropriate order using a mechanism similar to auto.msarima(), but implemented in auto.adam(). The values list(ar=...,i=...,ma=...) specify the maximum orders to check in this case. |

`...` |
Additional arguments passed to |

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