# seasonaldummy

##### Seasonal dummy variables

`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`

.

- Keywords
- ts

##### Usage

```
seasonaldummy(x,h)
fourier(x,K,h)
```

##### Arguments

- x
- Seasonal time series: a
`ts`

or a`msts`

object - h
- Number of periods ahead to forecast (optional)
- K
- Maximum order(s) of Fourier terms

##### Details

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.

##### Value

##### Examples

```
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)
```

*Documentation reproduced from package forecast, version 7.3, License: GPL (>= 2)*