fourier | R Documentation |
fourier
returns a matrix containing terms from a Fourier series, up
to order K
, suitable for use in Arima
,
auto.arima
, or tslm
.
fourier(x, K, h = NULL)
fourierf(x, K, h)
x |
Seasonal time series: a |
K |
Maximum order(s) of Fourier terms |
h |
Number of periods ahead to forecast (optional) |
fourierf
is deprecated, instead use the h
argument in
fourier
.
The period of the Fourier terms is determined from the time series
characteristics of x
. When h
is missing, the length of
x
also determines the number of rows for the matrix returned by
fourier
. Otherwise, the value of h
determines the number of
rows for the matrix returned by fourier
, typically used for
forecasting. The values within x
are not used.
Typical use would omit h
when generating Fourier terms for training a model
and include h
when generating Fourier terms for forecasting.
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
seasonaldummy
library(ggplot2)
# Using Fourier series for a "ts" object
# K is chosen to minimize the AICc
deaths.model <- auto.arima(USAccDeaths, xreg=fourier(USAccDeaths,K=5), seasonal=FALSE)
deaths.fcast <- forecast(deaths.model, xreg=fourier(USAccDeaths, K=5, h=36))
autoplot(deaths.fcast) + xlab("Year")
# 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)))
autoplot(taylor.fcast)
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