bayesforecast (version 1.0.5)

fourier: Fourier terms for modeling seasonality.

Description

fourier returns a matrix containing terms from a Fourier series, up to order K, suitable for use in arima or auto.sarima.

Usage

fourier(x, K, h = NULL)

Value

Numerical matrix.

Arguments

x

Seasonal time series: a ts or a msts object

K

Maximum order(s) of Fourier terms

h

Number of periods ahead to forecast (optional)

Author

Rob J Hyndman

Details

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 fitting 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.

See Also

seasonaldummy.

Examples

# \donttest{
 # Dynaimc Harmonic regression
 sf1 = auto.sarima(birth, xreg = fourier(birth,K= 6),iter = 500,chains = 1)
# }