3.6 The forecast package in R
This book uses the facilities in the forecast
package in R (which is loaded automatically whenever you load the fpp2
package). This appendix briefly summarises some of the features of the package. Please refer to the help files for individual functions to learn more, and to see some examples of their use.
Functions that output a forecast object:
Many functions, including meanf
, naive
, snaive
and rwf
, produce output in the form of a forecast
object (i.e., an object of class forecast
). This allows other functions (such as autoplot
) to work consistently across a range of forecasting models.
Objects of class forecast
contain information about the forecasting method, the data used, the point forecasts obtained, prediction intervals, residuals and fitted values. There are several functions designed to work with these objects including autoplot
, summary
and print
.
The following list shows all the functions that produce forecast
objects.
meanf()
naive()
,snaive()
rwf()
croston()
stlf()
ses()
holt()
,hw()
splinef()
thetaf()
forecast()
forecast()
function
So far we have used functions which produce a forecast
object directly. But a more common approach, which we will focus on in the rest of the book, will be to fit a model to the data, and then use the forecast()
function to produce forecasts from that model.
The forecast()
function works with many different types of input. It generally takes a time series or time series model as its main argument, and produces forecasts appropriately. It always returns objects of class forecast
.
If the first argument is of class ts
, it returns forecasts from the automatic ETS algorithm discussed in Chapter 7.
Here is a simple example, applying forecast()
to the ausbeer
data:
forecast(ausbeer)
#> Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
#> 2010 Q3 405 386 423 376 433
#> 2010 Q4 480 458 503 445 515
#> 2011 Q1 417 397 438 386 448
#> 2011 Q2 383 364 403 353 413
#> 2011 Q3 403 380 426 368 438
#> 2011 Q4 478 450 507 435 522
#> 2012 Q1 415 390 441 376 455
#> 2012 Q2 382 357 406 344 419
That works quite well if you have no idea what sort of model to use. But by the end of this book, you should not need to use forecast()
in this “blind” fashion. Instead, you will fit a model appropriate to the data, and then use forecast()
to produce forecasts from that model.