Version 2.2.12 [2018-05-04]
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.
- The theta model included in an ensemble can nowhandle seasonality with frequency >= 24.
- The ets model can now be included for hourly data.
- The “reshape2” package is no longer imported.
Version 2.1.11 [2018-03-27]
snaive() model to the ensemble. It is disabled by default, but can be added with “z”.
Version 2.0.10 [2018-01-03]
- API change in
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 cores
- Moved usage examples for
cvts() to the vignette.
- Add “GMDH” to suggested packages.
- Fixed a bug in
cvts() introduced in version 1.0.8 when a custom
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”.
- Refactored many unit tests and the vignette for quicker examples.
Version 1.1.9 [2017-08-23]
- Fixed a bug in
forecast.hybridModel() when for models where
xreg was not supplied to all of arima/nnetar models
- Fixes in unit tests and better documentation of unit tests
ts objects created with the “timekt” package can now be used in
forecast packages are now imported instead of loading their entire namespaces.
Version 1.0.8 [2017-07-10]
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.
- The package versioning now follows semantic versioning more closely; however, the convention used will be
- Instead of loading the entire
ggplot2 namespace, only specific functions are now imported.
Version 0.4.1 [2017-06-18]
- The “forecast” package v8.1 now declares the S3 method
accuracy(), so this is imported and no longer declared in “forecastHybrid”.
Version 0.4.0 [2017-03-31]
- Import the “zoo” package
- Fixed a bug in
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
- Documentation fixes and improvements, particularly for the
- Unit tests were optimized for speed, and the package tests in half the previous time.
- The behavior of the
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.
- Fixed a bug with
stlm component models when the
level argument was set to a single value instead of a vector of values.
- Fixed warning message for superfluous lists passed to base models in
Version 0.3.0 [2016-12-18]
- Prediction intervals are now created for
nnetar objects in the ensemble. This should address one aspect of incorrect prediction intervals (e.g. issue #37).
- theta models can be added (by including “
f” in the
models = argument for
hybridModel()) and are indeed part of the default - so by default, hybridModel() will now fit six models
accuracy.cvts() is now exported
plot.hybridModel() now supports
ggplot2 graphics when the argument
ggplot = TRUE is passed.
- Time series must be at least four observations long
- Fixed an error where e.args was passed to tbats instead of t.args
Version 0.2.0 [2016-09-23]
- Add timeseries cross validation with
- Add support for
weights = "cv.errors" in
- Fix model weights when
weights = "insample.errors" and one or more component models perfectly fit the time series
- Fixed erroneous warning message when
xreg is included in
n.args but a
nnetar model is not included in the model list
- Clean up titles in
- Enable passing
... arguments to
- Round weights in
print.hybridModel() to three digits for cleaner display
verbose argument and enable by default in
hybridModel() to display fitting/cross validation progress
Version 0.1.7 [2016-06-04]
- Build vignette with
knitr rmarkdown engine
- Build vignette with travis
Version 0.1.6 [2016-05-31]
- Fix broken S3 generic
- Add vignette
- Add NEWS
- Remove “fpp” from dependencies
- Fix warning for unimplemented parameter
weights = "cv.errors"
- Fix error with
stlm models when
2 * frequency(y) >= length(y)
- Documentation improvements MORE TODO
- Migrate unit tests away from deprecated
not() function from “testthat” package
- Add additional unit tests for bugfixes (accuracy fix, nnetar/stlm
2 * frequency(y) >= length(y),
weights = "cv.errors")
Version 0.1.5 [2016-04-16]