Title: Probabilistic Time Series Forecasting
Version: 0.1.1
Description: Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.
License: Apache License (≥ 2)
URL: https://github.com/Akai01/ngboostForecast
BugReports: https://github.com/Akai01/ngboostForecast/issues
Encoding: UTF-8
LazyData: true
SystemRequirements: Python (>= 3.6)
RoxygenNote: 7.2.0
Imports: dplyr (≥ 1.0.7), forecast (≥ 8.15), magrittr (≥ 2.0.1), R6 (≥ 2.5.1)
Suggests: ggplot2 (≥ 3.3.5), testthat (≥ 3.0.0)
Config/testthat/edition: 3
Config/reticulate: list( packages = list( list(package = 'importlib-metadata', pip = TRUE), list(package = 'ngboost', pip = TRUE)) )
Depends: R (≥ 3.6), reticulate (≥ 1.20)
NeedsCompilation: no
Packaged: 2022-08-06 10:57:52 UTC; akayr
Author: Resul Akay [aut, cre]
Maintainer: Resul Akay <resulakay1@gmail.com>
Repository: CRAN
Date/Publication: 2022-08-06 11:30:08 UTC

Pipe operator

Description

See magrittr::%>% for details.

See magrittr::%>% for details.

Usage

lhs %>% rhs

lhs %>% rhs

Value

Nothing

Assignment pipe operator

Description

See magrittr::%<>% for details.

Usage

lhs %<>% rhs

NGBoost distributions

Description

NGBoost distributions

Usage

Dist(
  dist = c("Normal", "Bernoulli", "k_categorical", "StudentT", "Laplace", "Cauchy",
    "Exponential", "LogNormal", "MultivariateNormal", "Poisson"),
  k
)

Arguments

dist

NGBoost distributions. One of the following:

  • Bernoulli

  • k_categorical

  • StudentT

  • Poisson

  • Laplace

  • Cauchy

  • Exponential

  • LogNormal

  • MultivariateNormal

  • Normal

k

Used only with k_categorical and MultivariateNormal

Value

An NGBoost Distribution object

NGBoost forecasting class

Description

The main forecasting class.

Value

An NGBforecast class

Methods

Public methods

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)

NGBoost forecasting model selection class

Description

It is a wrapper for the sklearn GridSearchCV with TimeSeriesSplit.

Methods

Public methods

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.

Author(s)

Resul Akay

References

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

Examples

## Not run: 

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


## End(Not run)

Select a rule to compare probabilistic predictions to the observed data.

Description

Select a rule to compare probabilistic predictions to the observed data. A score from ngboost.scores, e.g. LogScore.

Usage

Scores(score = c("LogScore", "CRPS", "CRPScore", "MLE"))

Arguments

score

A string. can be one of the following:

  • LogScore : Generic class for the log scoring rule.

  • CRPS : Generic class for the continuous ranked probability scoring rule.

  • CRPScore : Generic class for the continuous ranked probability scoring rule.

  • MLE : Generic class for the log scoring rule.

Value

A score class from ngboost.scores

Author(s)

Resul Akay

forecast package autolayer function

Description

See autolayer for details.

Usage

autolayer(object,...)

Value

A ggplot layer

See Also

autolayer

forecast package autoplot function

Description

See autoplot for details.

Usage

autoplot(object,...)

Value

A ggplot object

See Also

autoplot

Is conda installed?

Description

Only for internal usage.

Usage

is_exists_conda()

Value

Logical, TRUE if conda is installed.

Author(s)

Resul Akay

Probabilistic Time Series Forecasting

Description

Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.

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)

Road Casualties in Great Britain 1969-84

Description

The Seatbelts dataset from the datasets package.

Usage

seatbelts

Format

An object of class mts (inherits from ts) with 192 rows and 8 columns.

Source

Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, pp. 519–523.

Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford University Press.

https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/UKDriverDeaths.html

References

Harvey, A. C. and Durbin, J. (1986). The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series A, 149, 187–227.

Scikit-Learn interface

Description

Scikit-Learn interface

Usage

sklearner(module = "tree", class = "DecisionTreeRegressor", ...)

Arguments

module

scikit-learn module name, default is 'tree'.

class

scikit-learn's module class, default is 'DecisionTreeRegressor'

...

Other arguments passed to model class

Author(s)

Resul Akay

Examples

## Not run: 

sklearner(module = "tree", class = "DecisionTreeRegressor",
criterion="friedman_mse", min_samples_split=2)


## End(Not run)