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residuals.hybridModel() and added tests.Makefile targets for building and package
development.rolling argument to hybridModel()
that can be used when weights = "cv.errors" to control the
rolling argument in cvts().comb as an argument to
thiefModel().accuracy() formals to support changes in the
“forecast” package version 8.12.thief() function can
now be created with the new thiefModel() function. The API
is similar to that of hybridModel().cvts(). This results in significantly
faster execution and less memory usage, particularly when the
FUN and FCFUN functions are very quick
(e.g. snaive(), rwf(), stlm()),
the time series is short, few cores are used, or few CV folds run.cvts() examples.cvts().xreg argument passed in should now be a matrix
instead of a dataframe for consistency with “forecast” v8.5.hybridModel objects that use far less memory and that print
more cleanly to the console. For example, previously
hm <- hybridModel(wineind); format(object.size(hm), units = "auto")
produced a 5.8 Mb object but now it is only 314.8 Kb.hybridModel(). This can be
controlled by setting parallel = TRUE and setting
num.cores. By default this is not enabled since the
performance improvement typically only occurs when fitting
auto.arima and tbats models on long series
with large frequency (e.g. taylor).z.args for the snaive() model.tbats() and snaive() models now
respect and use lambda when passed in t.args
and z.args.snaive model are
now handled correctly.inst/davidshaub@gmx.com.key and hosted on both GitHub and
GitLab in pkg/inst/davidshaub@gmx.com.key.PI.combination argument to
forecast.hybridModel(). The default behavior is to follow
the existing methodology of using the most extreme prediction intervals
from the component models. When "mean" is passed instead, a
simple (unweighted) average of the component prediction intervals is
used instead.snaive() model to the ensemble. It is disabled by
default, but can be added with “z”.cvts() for the FCFUN
argument: custom forecasting functions should now return a S3 “forecast”
object with the point forecast in $mean, and the
ts properties should be properly set.cvts() now defaults to 2 corescvts() to the vignette.cvts() introduced in version 1.0.8 when
a custom FUN or FCFUN is used that requires
packages other than “forecast” or “forecastHybrid”.thetam() function now checks for an input time
series with less length than the seasonality. Similarly,
hybridModel() detects this behavior. Thanks to Nicholas
Fong for the bugfix.cvts() usage example in documentation for
“GMDH”.forecast.hybridModel() when for models
where xreg was not supplied to all of arima/nnetar
models.ts objects created with the “timekt” package can now be
used in hybridModel().doParallel and forecast packages are
now imported instead of loading their entire namespaces.cvts() now supports parallel fitting through the
num.cores argument. Note that if the model that you are
fitting also utilizes parallelization, the number of cores used by each
model multiplied by num.cores passed to cvts()
should not exceed the number of cores on your machine.MAJOR.MINOR.RELEASE_NUM.ggplot2 namespace, only
specific functions are now imported.accuracy(), so this is imported and no longer declared in
“forecastHybrid”.cvts() when using
rolling = TRUE whereby the incorrect number of periods were
calulated. Thanks to Ganesh Krishnan for the bugfix.
cvts() function now allows additional arguments to
be passed with .... Thanks to Ganesh Krishnan.... arguments can be passed to the
individual component models in forecast.hybridModel().cvts() function.forecast() function from the
“forecast” package when multiple or single prediction intervals are
passed has changed. The prediction inervals are now consistently
returned as matrices. This change fixes a bug in
forecast.hybridModel() when multiple prediction intervals
are used.forecast.hybridModel() for
ets, nnetar, and stlm component
models when the level argument was set to a single value
instead of a vector of values.hybridModel()nnetar objects
in the ensemble. This should address one aspect of incorrect prediction
intervals (e.g. issue #37).f” in the
models = argument for hybridModel()) and are
indeed part of the default - so by default, hybridModel() will now fit
six modelsaccuracy.cvts() is now exportedplot.hybridModel() now supports ggplot2
graphics when the argument ggplot = TRUE is passed.cvts()weights = "cv.errors" in
hybridModel()weights = "insample.errors" and
one or more component models perfectly fit the time seriesxreg is included
in n.args but a nnetar model is not included
in the model listplot.hybridModel()... arguments to plot()
from plot.hybridModel()print.hybridModel() to three digits
for cleaner displayverbose argument and enable by default in
hybridModel() to display fitting/cross validation
progressknitr rmarkdown engineaccuracy() and
hybridModel.accuracy()weights = "cv.errors"nnetar and stlm models when
2 * frequency(y) >= length(y)not() function
from “testthat” package2 * frequency(y) >= length(y),
weights = "cv.errors")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.