This week I uploaded a new version of the forecast package to CRAN. As there were a lot of changes, I decided to increase the version number to 6.0.
The changes are all outlined in the ChangeLog file as usual. I will highlight some of the more important changes since v5.0 here.
One of the most used functions in the package is
ets() and it provides a stock forecasting engine for many organizations. The default model selection is now restricted to exclude multiplicative trend models as these often give very poor forecasts due to the extrapolation of exponential trends. Multiplicative trend models can still be fitted if required. I compared the new default settings with the old defaults on the M3 data, and found a considerable difference in forecast accuracy:
Here “ETS” denotes the new default approach (without multiplicative trends) and “ETS (old)” is the old default approach including possibly multiplicative trends. For comparison, the results from applying
auto.arima to the same data are also shown.
auto.arima() function is now stricter on near unit-roots. Even if a model can be estimated, it will not be selected if the characteristic AR or MA roots are too close to the unit circle. This prevents occasional numerical instabilities occurring. Previously the roots had to be at least 0.001 away from the unit circle. Now they have to be at least 0.01 from the unit circle.
There is a new
allowmean argument in
auto.arima which can be used to prevent a mean term being included in a model.
There is a new
plot.Arima() function which plots the characteristic roots of an ARIMA model. This is based on a blog post I wrote last year.
It is now possible to easily obtain the fitted model order for use in other functions. The function
arimaorder applied to a fitted ARIMA model (such as that returned by
auto.arima) will return a numeric vector of the form (p,d,q) for a nonseasonal model and (p,d,q,P,D,Q,m) for a seasonal model. Similarly,
as.character applied to the object returned by
auto.arima will give a character string with the fitted model, suitable for use in plotting or reports.
The models returned by
bats were occasionally unstable. This problem has been fixed, again by restricting the roots to be further away from the unit circle.
forecast.stl combine forecasting with seasonal decomposition. The seasonally adjusted series are forecast, and then the forecasts are re-seasonalized. These functions now have a
forecastfunction argument to allow user-specified methods to be used in the forecasting step.
There is a new
stlm function and a corresponding
forecast.stlm function to allow the model estimation to be separated from the forecasting, thus matching most other forecasting methods in the package. This allows more flexible specification of the model to be used for the seasonally adjusted series.
Acf function replaces the
acf function to provide better plots of the autocorrelation function. The horizontal axis now highlights the seasonal lags.
I have added two new functions
taperedpacf to implement the estimates and plots proposed in this recent paper.
fourierf() functions produce a matrix of Fourier terms for use in regression models for seasonal time series. These were updated to work with
msts objects so that multiple seasonalities can be fitted.
Occasionally, the period of the seasonality may not be known. The
findfrequency() function will estimate it. This is based on an earlier version I wrote for this blog post.
forecast.ts() function takes a time series and returns some forecasts, without the user necessarily knowing what is going on under the hood. It will use
ets with default settings (if the data are non-seasonal or the seasonal period is 12 or less) and
stlf (if the seasonal period is 13 or more). If the seasonal period is unknown, there is an option (
find.frequency=TRUE) to estimate it first.