Description Usage Arguments Details Value Author(s) Examples
seasonaldummy
returns matrices of dummy variables suitable for use in arima
, lm
or tslm
. The last season is omitted and used as the control.
fourier
returns matrices containing terms from a Fourier series, up to order K
, suitable for use in arima
, lm
or tslm
.
fourierf
and seasonaldummyf
are deprecated, instead use the h
argument in fourier
and seasonaldummy
.
1 2 | seasonaldummy(x,h)
fourier(x,K,h)
|
x |
Seasonal time series: a |
h |
Number of periods ahead to forecast (optional) |
K |
Maximum order(s) of Fourier terms |
The number of dummy variables, or the period of the Fourier terms, is determined from the time series characteristics of x
. The length of x
also determines the number of rows for the matrices returned by seasonaldummy
and fourier
. The value of h
determines the number of rows for the matrices returned by seasonaldummy
and fourier
, typically used for forecasting. The values within x
are not used in any function.
When x
is a ts
object, the value of K
should be an integer and specifies the number of sine and cosine terms to return. Thus, the matrix returned has 2*K
columns.
When x
is a msts
object, then K
should be a vector of integers specifying the number of sine and cosine terms for each of the seasonal periods. Then the matrix returned will have 2*sum(K)
columns.
Numerical matrix.
Rob J Hyndman
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | plot(ldeaths)
# Using seasonal dummy variables
month <- seasonaldummy(ldeaths)
deaths.lm <- tslm(ldeaths ~ month)
tsdisplay(residuals(deaths.lm))
ldeaths.fcast <- forecast(deaths.lm,
data.frame(month=I(seasonaldummy(ldeaths,36))))
plot(ldeaths.fcast)
# A simpler approach to seasonal dummy variables
deaths.lm <- tslm(ldeaths ~ season)
ldeaths.fcast <- forecast(deaths.lm, h=36)
plot(ldeaths.fcast)
# Using Fourier series
deaths.lm <- tslm(ldeaths ~ fourier(ldeaths,3))
ldeaths.fcast <- forecast(deaths.lm,
data.frame(fourier(ldeaths,3,36)))
plot(ldeaths.fcast)
# Using Fourier series for a "msts" object
taylor.lm <- tslm(taylor ~ fourier(taylor, K = c(3, 3)))
taylor.fcast <- forecast(taylor.lm,
data.frame(fourier(taylor, K = c(3, 3), h = 270)))
plot(taylor.fcast)
|
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