View source: R/parsnip-arima_boost.R
auto_sarima_catboost_fit_impl | R Documentation |
Bridge ARIMA-Catboost Modeling function
auto_sarima_catboost_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, depth = 6, eta = 0.3, rsm = 1, iterations = 1000, min_data_in_leaf = 1, subsample = 1, ... )
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. |
depth |
The maximum depth of the tree (i.e. number of splits). |
eta |
The rate at which the boosting algorithm adapts from iteration-to-iteration. |
rsm |
The number of predictors that will be randomly sampled at each split when creating the tree models. |
iterations |
The number of trees contained in the ensemble. |
min_data_in_leaf |
The minimum number of data points in a node that is required for the node to be split further. |
subsample |
The amount of data exposed to the fitting routine. |
... |
Additional arguments passed to |
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