# auto_adam_fit_impl: Low-Level ADAM function for translating modeltime to forecast In modeltime: The Tidymodels Extension for Time Series Modeling

## Description

Low-Level ADAM function for translating modeltime to forecast

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```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, ... ) ```

## Arguments

 `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 `smooth::auto.adam`

modeltime documentation built on Oct. 18, 2021, 5:08 p.m.