Complex Exponential Smoothing (CES) model.
Calls smooth::ces()
from package smooth.
Dictionary
This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn()
:
Meta Information
Task type: “fcst”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”, “Date”
Required Packages: mlr3, mlr3forecast, smooth
Parameters
Id | Type | Default | Levels |
seasonality | character | none | none, simple, partial, full |
lags | untyped | - | |
initial | character | backcasting | backcasting, optimal, complete |
a | untyped | NULL | |
b | untyped | NULL | |
loss | character | likelihood | likelihood, MSE, MAE, HAM, MSEh, TMSE, GTMSE, MSCE |
holdout | logical | FALSE | TRUE, FALSE |
bounds | character | admissible | admissible, none |
silent | logical | TRUE | TRUE, FALSE |
regressors | character | use | use, select, adapt |
References
Svetunkov I (2023). “Smooth forecasting with the smooth package in R.” 2301.01790, https://arxiv.org/abs/2301.01790.
Svetunkov, Ivan (2023). Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM), 1st edition. Chapman and Hall/CRC. doi:10.1201/9781003452652 , https://openforecast.org/adam/.
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Package mlr3learners for a solid collection of essential learners.
Package mlr3extralearners for more learners.
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).mlr3pipelines to combine learners with pre- and postprocessing steps.
Package mlr3viz for some generic visualizations.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
LearnerFcst
,
mlr_learners_fcst.adam
,
mlr_learners_fcst.arfima
,
mlr_learners_fcst.arima
,
mlr_learners_fcst.auto_adam
,
mlr_learners_fcst.auto_arima
,
mlr_learners_fcst.auto_ces
,
mlr_learners_fcst.bats
,
mlr_learners_fcst.ets
,
mlr_learners_fcst.nnetar
,
mlr_learners_fcst.tbats
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> mlr3forecast::LearnerFcst
-> LearnerFcstCes
Methods
Inherited methods
mlr3::Learner$base_learner()
mlr3::Learner$configure()
mlr3::Learner$encapsulate()
mlr3::Learner$format()
mlr3::Learner$help()
mlr3::Learner$predict()
mlr3::Learner$predict_newdata()
mlr3::Learner$print()
mlr3::Learner$reset()
mlr3::Learner$selected_features()
mlr3::Learner$train()
mlr3::LearnerRegr$predict_newdata_fast()
Examples
# Define the Learner and set parameter values
learner = lrn("fcst.ces")
print(learner)
#>
#> ── <LearnerFcstCes> (fcst.ces): CES ────────────────────────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3forecast, and smooth
#> • Predict Types: [response]
#> • Feature Types: logical, integer, numeric, character, factor, ordered,
#> POSIXct, and Date
#> • Encapsulation: none (fallback: -)
#> • Properties: featureless and missings
#> • Other settings: use_weights = 'error'
# Define a Task
task = tsk("airpassengers")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# Print the model
print(learner$model)
#> Time elapsed: 0.03 seconds
#> Model estimated using ces() function: CES(none)
#> With backcasting initialisation
#> Distribution assumed in the model: Normal
#> Loss function type: likelihood; Loss function value: 438.0686
#> a0 + ia1: 1.9916+0.9958i
#>
#> Sample size: 96
#> Number of estimated parameters: 3
#> Number of degrees of freedom: 93
#> Information criteria:
#> AIC AICc BIC BICc
#> 882.1373 882.3981 889.8303 890.4257
# Importance method
if ("importance" %in% learner$properties) print(learner$importance)
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 21918.49