View source: R/parsnip-arima_boost.R
sarima_catboost_fit_impl | R Documentation |
Bridge ARIMA-Catboost Modeling function
sarima_catboost_fit_impl( x, y, period = "auto", p = 0, d = 0, q = 0, P = 0, D = 0, Q = 0, include.mean = TRUE, include.drift = FALSE, include.constant, lambda = model$lambda, biasadj = FALSE, method = c("CSS-ML", "ML", "CSS"), model = NULL, 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. |
p |
The order of the non-seasonal auto-regressive (AR) terms. |
d |
The order of integration for non-seasonal differencing. |
q |
The order of the non-seasonal moving average (MA) terms. |
P |
The order of the seasonal auto-regressive (SAR) terms. |
D |
The order of integration for seasonal differencing. |
Q |
The order of the seasonal moving average (SMA) terms. |
include.mean |
Should the ARIMA model include a mean term? The default
is |
include.drift |
Should the ARIMA model include a linear drift term?
(i.e., a linear regression with ARIMA errors is fitted.) The default is
|
include.constant |
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. |
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. |
model |
Output from a previous call to |
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 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.