View source: R/parsnip-arima_boost.R

auto_arima_xgboost_fit_impl | R Documentation |

Bridge ARIMA-XGBoost Modeling function

```
auto_arima_xgboost_fit_impl(
x,
y,
period = "auto",
max.p = 5,
max.d = 2,
max.q = 5,
max.P = 2,
max.D = 1,
max.Q = 2,
max.order = 5,
d = NA,
D = NA,
start.p = 2,
start.q = 2,
start.P = 1,
start.Q = 1,
stationary = FALSE,
seasonal = TRUE,
ic = c("aicc", "aic", "bic"),
stepwise = TRUE,
nmodels = 94,
trace = FALSE,
approximation = (length(x) > 150 | frequency(x) > 12),
method = NULL,
truncate = NULL,
test = c("kpss", "adf", "pp"),
test.args = list(),
seasonal.test = c("seas", "ocsb", "hegy", "ch"),
seasonal.test.args = list(),
allowdrift = TRUE,
allowmean = TRUE,
lambda = NULL,
biasadj = FALSE,
max_depth = 6,
nrounds = 15,
eta = 0.3,
colsample_bytree = NULL,
colsample_bynode = NULL,
min_child_weight = 1,
gamma = 0,
subsample = 1,
validation = 0,
early_stop = NULL,
...
)
```

`x` |
A dataframe of xreg (exogenous regressors) |

`y` |
A numeric vector of values to fit |

`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. |

`max.p` |
The maximum order of the non-seasonal auto-regressive (AR) terms. |

`max.d` |
The maximum order of integration for non-seasonal differencing. |

`max.q` |
The maximum order of the non-seasonal moving average (MA) terms. |

`max.P` |
The maximum order of the seasonal auto-regressive (SAR) terms. |

`max.D` |
The maximum order of integration for seasonal differencing. |

`max.Q` |
The maximum order of the seasonal moving average (SMA) terms. |

`max.order` |
Maximum value of p+q+P+Q if model selection is not stepwise. |

`d` |
Order of first-differencing. If missing, will choose a value based
on |

`D` |
Order of seasonal-differencing. If missing, will choose a value
based on |

`start.p` |
Starting value of p in stepwise procedure. |

`start.q` |
Starting value of q in stepwise procedure. |

`start.P` |
Starting value of P in stepwise procedure. |

`start.Q` |
Starting value of Q in stepwise procedure. |

`stationary` |
If |

`seasonal` |
If |

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

`stepwise` |
If |

`nmodels` |
Maximum number of models considered in the stepwise search. |

`trace` |
If |

`approximation` |
If |

`method` |
fitting method: maximum likelihood or minimize conditional sum-of-squares. The default (unless there are missing values) is to use conditional-sum-of-squares to find starting values, then maximum likelihood. Can be abbreviated. |

`truncate` |
An integer value indicating how many observations to use in
model selection. The last |

`test` |
Type of unit root test to use. See |

`test.args` |
Additional arguments to be passed to the unit root test. |

`seasonal.test` |
This determines which method is used to select the number of seasonal differences. The default method is to use a measure of seasonal strength computed from an STL decomposition. Other possibilities involve seasonal unit root tests. |

`seasonal.test.args` |
Additional arguments to be passed to the seasonal
unit root test.
See |

`allowdrift` |
If |

`allowmean` |
If |

`lambda` |
Box-Cox transformation parameter. If |

`biasadj` |
Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values. |

`max_depth` |
An integer for the maximum depth of the tree. |

`nrounds` |
An integer for the number of boosting iterations. |

`eta` |
A numeric value between zero and one to control the learning rate. |

`colsample_bytree` |
Subsampling proportion of columns. |

`colsample_bynode` |
Subsampling proportion of columns for each node
within each tree. See the |

`min_child_weight` |
A numeric value for the minimum sum of instance weights needed in a child to continue to split. |

`gamma` |
A number for the minimum loss reduction required to make a further partition on a leaf node of the tree |

`subsample` |
Subsampling proportion of rows. |

`validation` |
A positive number. If on |

`early_stop` |
An integer or |

`...` |
Additional arguments passed to |

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