ngboostForecast (version 0.1.1)

NGBforecastCV: NGBoost forecasting model selection class

Description

It is a wrapper for the sklearn GridSearchCV with TimeSeriesSplit.

Author

Resul Akay

Methods

Public methods

• NGBforecastCV$new()

• NGBforecastCV$tune()

• NGBforecastCV$clone()

---

Method new()

Initialize an NGBforecastCV model.

Usage

NGBforecastCV$new(
  Dist = NULL,
  Score = NULL,
  Base = NULL,
  natural_gradient = TRUE,
  n_estimators = as.integer(500),
  learning_rate = 0.01,
  minibatch_frac = 1,
  col_sample = 1,
  verbose = TRUE,
  verbose_eval = as.integer(100),
  tol = 1e-04,
  random_state = NULL
)

Arguments

Dist

Assumed distributional form of Y|X=x. An output of Dist function, e.g. Dist('Normal')

Score

Rule to compare probabilistic predictions to the observed data. A score from Scores function, e.g. Scores(score = "LogScore").

Base

Base learner. An output of sklearner function, e.g. sklearner(module = "tree", class = "DecisionTreeRegressor", ...)

natural_gradient

Logical flag indicating whether the natural gradient should be used

n_estimators

The number of boosting iterations to fit

learning_rate

The learning rate

minibatch_frac

The percent subsample of rows to use in each boosting iteration

col_sample

The percent subsample of columns to use in each boosting iteration

verbose

Flag indicating whether output should be printed during fitting. If TRUE it will print logs.

verbose_eval

Increment (in boosting iterations) at which output should be printed

tol

Numerical tolerance to be used in optimization

random_state

Seed for reproducibility.

Returns

An NGBforecastCV object that can be fit.

---

Method tune()

Tune ngboosForecast.

Usage

NGBforecastCV$tune(
  y,
  max_lag = 5,
  xreg = NULL,
  seasonal = TRUE,
  K = frequency(y)/2 - 1,
  n_splits = NULL,
  train_loss_monitor = NULL,
  val_loss_monitor = NULL,
  early_stopping_rounds = NULL
)

Arguments

y

A time series (ts) object

max_lag

Maximum number of lags

xreg

Optional. A numerical matrix of external regressors, which must have the same number of rows as y.

seasonal

Boolean. If seasonal = TRUE the fourier terms will be used for modeling seasonality.

K

Maximum order(s) of Fourier terms, used only if seasonal = TRUE.

n_splits

Number of splits. Must be at least 2.

train_loss_monitor

A custom score or set of scores to track on the training set during training. Defaults to the score defined in the NGBoost constructor. Please do not modify unless you know what you are doing.

val_loss_monitor

A custom score or set of scores to track on the validation set during training. Defaults to the score defined in the NGBoost constructor. Please do not modify unless you know what you are doing.

early_stopping_rounds

The number of consecutive boosting iterations during which the loss has to increase before the algorithm stops early.

test_size

The length of validation set. If it is NULL, then, it is automatically specified.

Returns

A named list of best parameters.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

NGBforecastCV$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

https://stanfordmlgroup.github.io/ngboost/2-tuning.html

Examples

if (FALSE) {

library(ngboostForecast)

dists <- list(Dist("Normal"))

base_learners <- list(sklearner(module = "tree", class = "DecisionTreeRegressor",
                                max_depth = 1),
                      sklearner(module = "tree", class = "DecisionTreeRegressor",
                                max_depth = 2),
                      sklearner(module = "tree", class = "DecisionTreeRegressor",
                                max_depth = 3),
                      sklearner(module = "tree", class = "DecisionTreeRegressor",
                                max_depth = 4),
                      sklearner(module = "tree", class = "DecisionTreeRegressor",
                                max_depth = 5),
                      sklearner(module = "tree", class = "DecisionTreeRegressor",
                                max_depth = 6),
                      sklearner(module = "tree", class = "DecisionTreeRegressor",
                                max_depth = 7))

scores <-  list(Scores("LogScore"))

model <- NGBforecastCV$new(Dist = dists,
                           Base = base_learners,
                           Score = scores,
                           natural_gradient = TRUE,
                           n_estimators = list(10, 100),
                           learning_rate = list(0.1, 0.2),
                           minibatch_frac = list(0.1, 1),
                           col_sample = list(0.3),
                           verbose = FALSE,
                           verbose_eval = 100,
                           tol = 1e-5)

params <- model$tune(y = AirPassengers,
seasonal = TRUE,
max_lag = 12,
xreg = NULL,
early_stopping_rounds = NULL,
n_splits = 4L)

params

}