NGBforecast: NGBoost forecasting class

In ngboostForecast: Probabilistic Time Series Forecasting

NGBoost forecasting class

Description

The main forecasting class.

Value

An NGBforecast class

Methods

Public methods

• NGBforecast$new()

• NGBforecast$fit()

• NGBforecast$forecast()

• NGBforecast$feature_importances()

• NGBforecast$plot_feature_importance()

• NGBforecast$get_params()

• NGBforecast$clone()

---

Method new()

Initialize an NGBforecast model.

Usage

NGBforecast$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 NGBforecast object that can be fit.

---

Method fit()

Fit the initialized model.

Usage

NGBforecast$fit(
  y,
  max_lag = 5,
  xreg = NULL,
  test_size = NULL,
  seasonal = TRUE,
  K = frequency(y)/2 - 1,
  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.

test_size

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

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.

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.

Returns

NULL

---

Method forecast()

Forecast the fitted model

Usage

NGBforecast$forecast(h = 6, xreg = NULL, level = c(80, 95), data_frame = FALSE)

Arguments

h

Forecast horizon

xreg

A numerical vector or matrix of external regressors

level

Confidence level for prediction intervals

data_frame

Bool. If TRUE, forecast will be returned as a data.frame object, if FALSE it will return a forecast class. If TRUE, autoplot will function.

---

Method feature_importances()

Return the feature importance for all parameters in the distribution (the higher, the more important the feature).

Usage

NGBforecast$feature_importances()

Returns

A data frame

---

Method plot_feature_importance()

Plot feature importance

Usage

NGBforecast$plot_feature_importance()

Returns

A ggplot object

---

Method get_params()

Get parameters for this estimator.

Usage

NGBforecast$get_params(deep = TRUE)

Arguments

deep

bool, default = TRUE If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

A named list of parameters.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

NGBforecast$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

Resul Akay

References

Duan, T et. al. (2019), NGBoost: Natural Gradient Boosting for Probabilistic Prediction.

Examples

## Not run: 

library(ngboostForecast)

model <- NGBforecast$new(Dist = Dist("Normal"),
                         Base = sklearner(module = "linear_model",
                         class = "Ridge"),
                         Score = Scores("LogScore"),
                         natural_gradient = TRUE,
                         n_estimators = 200,
                         learning_rate =  0.1,
                         minibatch_frac = 1,
                         col_sample = 1,
                         verbose = TRUE,
                         verbose_eval = 100,
                         tol = 1e-5)
model$fit(y = AirPassengers, seasonal = TRUE, max_lag = 12, xreg = NULL,
early_stopping_rounds = 10L)
fc <- model$forecast(h = 12, level = c(90, 80), xreg = NULL)

autoplot(fc)

## End(Not run)

ngboostForecast documentation built on Aug. 6, 2022, 5:07 p.m.