vignettes/fable.Rmd
+ Source: vignettes/fable.Rmd
fable.Rmdvignettes/transformations.Rmd
+ Source: vignettes/transformations.Rmd
transformations.Rmd
library(fable)
#> Loading required package: fabletools
+#> Registered S3 method overwritten by 'tsibble':
+#> method from
+#> as_tibble.grouped_df dplyr
library(tsibble)
#>
#> Attaching package: 'tsibble'
@@ -148,12 +149,12 @@ Combining transformations
Multiple transformations can be combined using this interface,
allowing more complicated transformations to be used. A simple example
-of a combined transformation is \(f(x) =
-log(x+1)\), as it involves both a log
-transformation, and a +1 transformation. This
-transformation is commonly used to overcome a limitation of using log
-transformations to preserve non-negativity, on data which contains
-zeroes.
+of a combined transformation is
+,
+as it involves both a log transformation, and a
++1 transformation. This transformation is commonly used to
+overcome a limitation of using log transformations to preserve
+non-negativity, on data which contains zeroes.
Simple combined transformations and backtransformations can be
constructed automatically.
@@ -181,18 +182,22 @@ Custom transformationsforecasting
within limits). The appropriate transformation to ensure the
-forecasted values are between \(a\) and
-\(b\) (where \(a<b\)) is given by:
-
\[f(x) =
-\log\left(\dfrac{x-a}{b-x}\right)\]
+forecasted values are between
+
+and
+
+(where
+)
+is given by:
+
Inverting this transformation gives the appropriate
back-transformation of:
-\[f^{-1}(x) = \dfrac{a + be^x}{1 + e^x} =
-\dfrac{(b-a)e^x}{1 + e^x} + a\] To use this transformation for
-modelling, we can pair the transformation with its back transformation
-using the new_transformation function from
-fabletools. This function which accepts two inputs: first
-the transformation, and second the back-transformation.
+
+To use this transformation for modelling, we can pair the transformation
+with its back transformation using the new_transformation
+function from fabletools. This function which accepts two
+inputs: first the transformation, and second the
+back-transformation.
scaled_logit <- function(x, lower=0, upper=1){
log((x-lower)/(upper-x))
@@ -237,12 +242,13 @@ Forecast means and medians
-\[\hat{y} = f^{-1}(\tilde{y}) +
-\dfrac{1}{2}\sigma^2\dfrac{\partial^2}{\partial
-\tilde{y}^2}f^{-1}(\tilde{y})\] Note that the forecast medians
-are given by \(f^{-1}(\tilde{y})\), and
-the adjustment needed to produce forecast means (\(\hat{y}\)) is \(\dfrac{1}{2}\sigma^2\dfrac{\partial^2}{\partial
-\tilde{y}^2}f^{-1}(\tilde{y})\).
+
+Note that the forecast medians are given by
+,
+and the adjustment needed to produce forecast means
+()
+is
+.
The fable package automatically produces forecast means (by
back-transforming and adjusting the transformed forecasts). The forecast
medians can be obtained via the forecast intervals when
@@ -255,9 +261,7 @@
Forecast means and medians
-
-
+
Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.1.0.
O'Hara-Wild M, Hyndman R, Wang E (2024). fable: Forecasting Models for Tidy Time Series. -R package version 0.3.4.9000, https://github.com/tidyverts/fable, https://fable.tidyverts.org. +R package version 0.3.4.9000, https://github.com/tidyverts/fable, https://fable.tidyverts.org.
@Manual{,
title = {fable: Forecasting Models for Tidy Time Series},
@@ -124,15 +124,15 @@ Citation
A model specification.
+A model specification.
Model specification (see "Specials" section).
The information criterion used in selecting the model.
A function used to compute a metric from an Arima
object which is minimised to select the best model.
Should stepwise be used? (Stepwise can be much faster)
Should the stepwise search move to the next best option immediately?
Should CSS (conditional sum of squares) be used during model
selection? The default (NULL) will use the approximation if there are more than
150 observations or if the seasonal period is greater than 12.
A logical predicate on the orders of p, d, q,
P, D, Q and constant to consider in the search. See "Specials" for
the meaning of these terms.
A specification of unit root tests to use in the
selection of d and D. See unitroot_options() for more details.
If TRUE, the selection_metric of estimated models in the
selection procedure will be outputted to the console.
Further arguments for stats::arima()
A model specification.
+A model specification.
The fable ARIMA() function uses an alternate parameterisation of constants
-to stats::arima() and forecast::Arima(). While the parameterisations
-are equivalent, the coefficients for the constant/mean will differ.
In fable, the parameterisation used is:
The fable ARIMA() function uses an alternative parameterisation of
+constants to stats::arima() and forecast::Arima(). While the
+parameterisations are equivalent, the coefficients for the constant/mean
+will differ.
In fable, if there are no exogenous regressors, the parameterisation used
+is:
$$(1-\phi_1B - \cdots - \phi_p B^p)(1-B)^d y_t = c + (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t$$
In stats and forecast, an ARIMA model is parameterised as:
$$(1-\phi_1B - \cdots - \phi_p B^p)(y_t' - \mu) = (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t$$
where \(\mu\) is the mean of \((1-B)^d y_t\) and \(c = \mu(1-\phi_1 - \cdots - \phi_p )\).
+If there are exogenous regressors, fable uses the same parameterisation as
+used in stats and forecast. That is, it fits a regression with ARIMA(p,d,q)
+errors:
$$y_t = c + \beta' x_t + z_t$$
+where \(\beta\) is a vector of regression coefficients, \(x_t\) is +a vector of exogenous regressors at time \(t\), and \(z_t\) is an +ARIMA(p,d,q) error process:
+$$(1-\phi_1B - \cdots - \phi_p B^p)(1-B)^d z_t = (1 + \theta_1 B + \cdots + \theta_q B^q)\varepsilon_t$$
+For details of the estimation algorithm, see the
+arima function in the stats package.
The specials define the space over which ARIMA will search for the model that best fits the data. If the RHS of formula is left blank, the default search space is given by pdq() + PDQ(): that is, a model with candidate seasonal and nonseasonal terms, but no exogenous regressors. Note that a seasonal model requires at least 2 full seasons of data; if this is not available, ARIMA will revert to a nonseasonal model with a warning.
Model specification (see "Specials" section).
The optimisation criterion used to optimise the parameters.
Which variant of Croston's method to use. Defaults to "croston" for
Croston's method, but can also be set to "sba" for the Syntetos-Boylan
approximation, and "sbj" for the Shale-Boylan-Johnston method.
Not used.
A model specification.
+A model specification.
Model specification (see "Specials" section).
The optimization criterion. Defaults to the log-likelihood
"lik", but can also be set to "mse" (Mean Square Error), "amse"
(Average MSE over first nmse forecast horizons), "sigma" (Standard
deviation of residuals), or "mae" (Mean Absolute Error).
If opt_crit == "amse", nmse provides the number of steps for
average multistep MSE (1<=nmse<=30).
Type of parameter space to impose: "usual" indicates
all parameters must lie between specified lower and upper bounds;
"admissible" indicates parameters must lie in the admissible space;
"both" (default) takes the intersection of these regions.
The information criterion used in selecting the model.
If TRUE (default), the models with infinite variance will not be allowed. These restricted model components are AMM, AAM, AMA, and MMA.
Other arguments
A model specification.
+A model specification.
The specials define the methods and parameters for the components (error, trend, and seasonality) of an ETS model. If more than one method is specified, ETS will consider all combinations of the specified models and select the model which best fits the data (minimising ic). The method argument for each specials have reasonable defaults, so if a component is not specified an appropriate method will be chosen automatically.
Hyndman, R.J., Koehler, A.B., Snyder, R.D., and Grose, S. (2002) "A state space framework for automatic forecasting using exponential smoothing methods", International J. Forecasting, 18(3), -439--454.
+439–454.Hyndman, R.J., Akram, Md., and Archibald, B. (2008) "The admissible parameter space for exponential smoothing models". Annals of -Statistical Mathematics, 60(2), 407--426.
+Statistical Mathematics, 60(2), 407–426.Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag. http://www.exponentialsmoothing.net.
@@ -205,15 +205,15 @@A model specification.
+A model specification.
Model specification (see "Specials" section).
Number of nodes in the hidden layer. Default is half of the number of input nodes (including external regressors, if given) plus 1.
Number of networks to fit with different random starting weights. These are then averaged when producing forecasts.
If TRUE, inputs are scaled by subtracting the column means and dividing by their respective standard deviations. Scaling is applied after transformations.
Other arguments passed to \link[nnet]{nnet}.
A model specification.
+A model specification.
A model specification.
+A model specification.
A model specification.
+A model specification.
A model specification.
+A model specification.
A model specification.
+A model specification.
breusch_godfrey(x, ...)
-# S3 method for TSLM
+# S3 method for class 'TSLM'
breusch_godfrey(x, order = 1, type = c("Chisq", "F"), ...)# S3 method for ETS
+ # S3 method for class 'ETS'
components(object, ...)
A fabletools::dable() containing estimated states.
A fabletools::dable() containing estimated states.
R/fable-package.R
+ Source: R/fable-package.R
fable-package.Rd# S3 method for AR
+ # S3 method for class 'AR'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for ARIMA
+ # S3 method for class 'ARIMA'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for ETS
+ # S3 method for class 'ETS'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for NNETAR
+ # S3 method for class 'NNETAR'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for RW
+ # S3 method for class 'RW'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for TSLM
+ # S3 method for class 'TSLM'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for VAR
+ # S3 method for class 'VAR'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
R/croston.R
+ Source: R/croston.R
fitted.croston.Rd# S3 method for croston
+ # S3 method for class 'croston'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
R/theta.R
+ Source: R/theta.R
fitted.fable_theta.Rd# S3 method for fable_theta
+ # S3 method for class 'fable_theta'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for model_mean
+ # S3 method for class 'model_mean'
fitted(object, ...)
A vector of fitted values.
+A vector of fitted values.
# S3 method for AR
+ # S3 method for class 'AR'
forecast(
object,
new_data = NULL,
@@ -82,35 +82,35 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- times
+- times
The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -138,15 +138,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.ARIMA.html b/dev/reference/forecast.ARIMA.html
index 24c591d9..34565c88 100644
--- a/dev/reference/forecast.ARIMA.html
+++ b/dev/reference/forecast.ARIMA.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.ARIMA • fable Forecast a model from the fable package — forecast.ARIMA • fable
-
+
# S3 method for ARIMA
+ # S3 method for class 'ARIMA'
forecast(
object,
new_data = NULL,
@@ -82,35 +82,35 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- times
+- times
The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -148,15 +148,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.ETS.html b/dev/reference/forecast.ETS.html
index d5ab41e7..e5b40dc4 100644
--- a/dev/reference/forecast.ETS.html
+++ b/dev/reference/forecast.ETS.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.ETS • fable Forecast a model from the fable package — forecast.ETS • fable
-
+
# S3 method for ETS
+ # S3 method for class 'ETS'
forecast(
object,
new_data,
@@ -83,39 +83,39 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- simulate
+- simulate
If TRUE, prediction intervals are produced by simulation rather than using analytic formulae.
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- times
+- times
The number of sample paths to use in estimating the forecast distribution if simulated intervals are used.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -152,15 +152,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.NNETAR.html b/dev/reference/forecast.NNETAR.html
index d93267bb..78ec6809 100644
--- a/dev/reference/forecast.NNETAR.html
+++ b/dev/reference/forecast.NNETAR.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.NNETAR • fable Forecast a model from the fable package — forecast.NNETAR • fable
-
+
R/nnetar.R
+ Source: R/nnetar.R
forecast.NNETAR.Rd# S3 method for NNETAR
+ # S3 method for class 'NNETAR'
forecast(
object,
new_data,
@@ -83,39 +83,39 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- simulate
+- simulate
If TRUE, forecast distributions are produced by sampling from a normal distribution. Without simulation, forecast uncertainty cannot be estimated for this model and instead a degenerate distribution with the forecast mean will be produced.
- bootstrap
+- bootstrap
If TRUE, forecast distributions are produced by sampling from the model's training residuals.
- times
+- times
The number of sample paths to use in producing the forecast distribution. Setting simulate = FALSE or times = 0 will produce degenerate forecast distributions of the forecast mean.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -143,15 +143,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.RW.html b/dev/reference/forecast.RW.html
index 7d01000c..d74e549f 100644
--- a/dev/reference/forecast.RW.html
+++ b/dev/reference/forecast.RW.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.RW • fable Forecast a model from the fable package — forecast.RW • fable
-
+
# S3 method for RW
+ # S3 method for class 'RW'
forecast(
object,
new_data,
@@ -83,39 +83,39 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- simulate
+- simulate
If TRUE, prediction intervals are produced by simulation rather than using analytic formulae.
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- times
+- times
The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -160,15 +160,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.TSLM.html b/dev/reference/forecast.TSLM.html
index 2124ffca..3cb895b5 100644
--- a/dev/reference/forecast.TSLM.html
+++ b/dev/reference/forecast.TSLM.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.TSLM • fable Forecast a model from the fable package — forecast.TSLM • fable
-
+
# S3 method for TSLM
+ # S3 method for class 'TSLM'
forecast(
object,
new_data,
@@ -83,23 +83,25 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- approx_normal
+- approx_normal
Should the resulting forecast distributions be
approximated as a Normal distribution instead of a Student's T
distribution. Returning Normal distributions (the default) is a useful
@@ -107,19 +109,17 @@
Arguments
or reconciliation processes.- times
+- times
The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -156,15 +156,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.VAR.html b/dev/reference/forecast.VAR.html
index e0f0139f..dfe2bd7b 100644
--- a/dev/reference/forecast.VAR.html
+++ b/dev/reference/forecast.VAR.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.VAR • fable Forecast a model from the fable package — forecast.VAR • fable
-
+
# S3 method for VAR
+ # S3 method for class 'VAR'
forecast(
object,
new_data = NULL,
@@ -82,35 +82,35 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- times
+- times
The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -151,15 +151,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.croston.html b/dev/reference/forecast.croston.html
index a87b84e4..a9183033 100644
--- a/dev/reference/forecast.croston.html
+++ b/dev/reference/forecast.croston.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.croston • fable Forecast a model from the fable package — forecast.croston • fable
-
+
R/croston.R
+ Source: R/croston.R
forecast.croston.Rd# S3 method for croston
+ # S3 method for class 'croston'
forecast(object, new_data, specials = NULL, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
Other arguments passed to methods
A list of forecasts.
+A list of forecasts.
R/theta.R
+ Source: R/theta.R
forecast.fable_theta.Rd# S3 method for fable_theta
+ # S3 method for class 'fable_theta'
forecast(
object,
new_data,
@@ -82,35 +82,35 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- times
+- times
The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -148,15 +148,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/forecast.model_mean.html b/dev/reference/forecast.model_mean.html
index 7554a9f0..a70d8fcb 100644
--- a/dev/reference/forecast.model_mean.html
+++ b/dev/reference/forecast.model_mean.html
@@ -1,9 +1,9 @@
-Forecast a model from the fable package — forecast.model_mean • fable Forecast a model from the fable package — forecast.model_mean • fable
-
+
R/mean.R
+ Source: R/mean.R
forecast.model_mean.Rd# S3 method for model_mean
+ # S3 method for class 'model_mean'
forecast(
object,
new_data,
@@ -82,35 +82,35 @@ Forecast a model from the fable package
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- bootstrap
+- bootstrap
If TRUE, then forecast distributions are computed using simulation with resampled errors.
- times
+- times
The number of sample paths to use in estimating the forecast distribution when bootstrap = TRUE.
- ...
+- ...
Other arguments passed to methods
Value
-
-
-A list of forecasts.
+ A list of forecasts.
@@ -141,15 +141,15 @@ Examples
-
-
+
+
diff --git a/dev/reference/generate.AR.html b/dev/reference/generate.AR.html
index 8509594f..9aaa7c79 100644
--- a/dev/reference/generate.AR.html
+++ b/dev/reference/generate.AR.html
@@ -1,12 +1,12 @@
-Generate new data from a fable model — generate.AR • fable Generate new data from a fable model — generate.AR • fable
-
+
# S3 method for AR
+ # S3 method for class 'AR'
generate(x, new_data = NULL, specials = NULL, bootstrap = FALSE, ...)
A fitted model.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, then forecast distributions are computed using simulation with resampled errors.
Other arguments passed to methods
# S3 method for ARIMA
+ # S3 method for class 'ARIMA'
generate(x, new_data, specials, bootstrap = FALSE, ...)
A fitted model.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, then forecast distributions are computed using simulation with resampled errors.
Other arguments passed to methods
# S3 method for ETS
+ # S3 method for class 'ETS'
generate(x, new_data, specials, bootstrap = FALSE, ...)
A fitted model.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, then forecast distributions are computed using simulation with resampled errors.
Other arguments passed to methods
# S3 method for NNETAR
+ # S3 method for class 'NNETAR'
generate(x, new_data, specials = NULL, bootstrap = FALSE, ...)
A fitted model.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, then forecast distributions are computed using simulation with resampled errors.
Other arguments passed to methods
# S3 method for RW
+ # S3 method for class 'RW'
generate(x, new_data, bootstrap = FALSE, ...)
# S3 method for TSLM
+ # S3 method for class 'TSLM'
generate(x, new_data, specials, bootstrap = FALSE, ...)
A fitted model.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, then forecast distributions are computed using simulation with resampled errors.
Other arguments passed to methods
# S3 method for model_mean
+ # S3 method for class 'model_mean'
generate(x, new_data, bootstrap = FALSE, ...)
# S3 method for AR
+ # S3 method for class 'AR'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for ARIMA
+ # S3 method for class 'ARIMA'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for ETS
+ # S3 method for class 'ETS'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for NNETAR
+ # S3 method for class 'NNETAR'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for RW
+ # S3 method for class 'RW'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for TSLM
+ # S3 method for class 'TSLM'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for VAR
+ # S3 method for class 'VAR'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for fable_theta
+ # S3 method for class 'fable_theta'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
# S3 method for model_mean
+ # S3 method for class 'model_mean'
glance(x, ...)
A one row tibble summarising the model's fit.
+A one row tibble summarising the model's fit.
Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.1.0.
R/arima.R
+ Source: R/arima.R
interpolate.ARIMA.Rd# S3 method for ARIMA
+ # S3 method for class 'ARIMA'
interpolate(object, new_data, specials, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
Other arguments passed to methods
A tibble of the same dimension of new_data with missing values interpolated.
A tibble of the same dimension of new_data with missing values interpolated.
# S3 method for TSLM
+ # S3 method for class 'TSLM'
interpolate(object, new_data, specials, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
Other arguments passed to methods
A tibble of the same dimension of new_data with missing values interpolated.
A tibble of the same dimension of new_data with missing values interpolated.
R/mean.R
+ Source: R/mean.R
interpolate.model_mean.Rd# S3 method for model_mean
+ # S3 method for class 'model_mean'
interpolate(object, new_data, specials, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
Other arguments passed to methods
A tibble of the same dimension of new_data with missing values interpolated.
A tibble of the same dimension of new_data with missing values interpolated.
# S3 method for AR
+ # S3 method for class 'AR'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, the coefficients for the fitted model will be re-estimated to suit the new data.
Other arguments passed to methods
A refitted model.
+A refitted model.
# S3 method for ARIMA
+ # S3 method for class 'ARIMA'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, the coefficients for the fitted model will be re-estimated to suit the new data.
Other arguments passed to methods
A refitted model.
+A refitted model.
# S3 method for ETS
+ # S3 method for class 'ETS'
refit(
object,
new_data,
@@ -82,27 +82,29 @@ Refit an ETS model
Arguments
- - object
+
+
+- object
A model for which forecasts are required.
- new_data
+- new_data
A tsibble containing the time points and exogenous regressors to produce forecasts for.
- specials
+- specials
(passed by fabletools::forecast.mdl_df()).
- reestimate
+- reestimate
If TRUE, the coefficients for the fitted model will be re-estimated to suit the new data.
- reinitialise
+- reinitialise
If TRUE, the initial parameters will be re-estimated to suit the new data.
- ...
+- ...
Other arguments passed to methods
@@ -168,15 +170,15 @@ Examples
# S3 method for NNETAR
+ # S3 method for class 'NNETAR'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, the networks will be initialized with random
starting weights to suit the new data. If FALSE, for every network the best
individual set of weights found in the pre-estimation process is used as the
starting weight vector.
Other arguments passed to methods
A refitted model.
+A refitted model.
# S3 method for RW
+ # S3 method for class 'RW'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, the lag walk model will be re-estimated
to suit the new data.
Other arguments passed to methods
# S3 method for TSLM
+ # S3 method for class 'TSLM'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, the coefficients for the fitted model will be re-estimated to suit the new data.
Other arguments passed to methods
# S3 method for model_mean
+ # S3 method for class 'model_mean'
refit(object, new_data, specials = NULL, reestimate = FALSE, ...)
A model for which forecasts are required.
A tsibble containing the time points and exogenous regressors to produce forecasts for.
(passed by fabletools::forecast.mdl_df()).
If TRUE, the mean for the fitted model will be re-estimated
to suit the new data.
Other arguments passed to methods
# S3 method for AR
+
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for ARIMA
+
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for ETS
+ # S3 method for class 'ETS'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for NNETAR
+ # S3 method for class 'NNETAR'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for RW
+ # S3 method for class 'RW'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for TSLM
+ # S3 method for class 'TSLM'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for VAR
+ # S3 method for class 'VAR'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
R/croston.R
+ Source: R/croston.R
residuals.croston.Rd# S3 method for croston
+ # S3 method for class 'croston'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
R/theta.R
+ Source: R/theta.R
residuals.fable_theta.Rd# S3 method for fable_theta
+ # S3 method for class 'fable_theta'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for model_mean
+ # S3 method for class 'model_mean'
residuals(object, ...)
A vector of fitted residuals.
+A vector of fitted residuals.
# S3 method for AR
+ # S3 method for class 'AR'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for ARIMA
+ # S3 method for class 'ARIMA'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for ETS
+ # S3 method for class 'ETS'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for NNETAR
+ # S3 method for class 'NNETAR'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for RW
+ # S3 method for class 'RW'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for TSLM
+ # S3 method for class 'TSLM'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for VAR
+ # S3 method for class 'VAR'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for croston
+ # S3 method for class 'croston'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for fable_theta
+ # S3 method for class 'fable_theta'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
# S3 method for model_mean
+ # S3 method for class 'model_mean'
tidy(x, ...)
An object to be converted into a tidy tibble::tibble().
Additional arguments to tidying method.
The model's coefficients in a tibble.
The model's coefficients in a tibble.
R/arima.R
+ Source: R/arima.R
unitroot_options.RdThe level for the test specified in the pval functions. As long as pval < alpha, differences will be added.
A function (or lambda expression) that provides a p-value for the unit root test. As long as pval < alpha, differences will be added.
For the function for the seasonal p-value, the seasonal period will be provided as the .period argument to this function.
A vector of data to test is available as . or .x.
A list of parameters
+A list of parameters