I’ve received a few emails about including regression variables (i.e., covariates) in TBATS models. As TBATS models are related to ETS models, `tbats()`

is unlikely to ever include covariates as explained here. It won’t actually complain if you include an `xreg`

argument, but it will ignore it.

When I want to include covariates in a time series model, I tend to use `auto.arima()`

with covariates included via the `xreg`

argument. If the time series has multiple seasonal periods, I use Fourier terms as additional covariates. See my post on forecasting daily data for some discussion of this model. Note that `fourier()`

and `fourierf()`

now handle `msts`

objects, so it is very simple to do this.

For example, if `holiday`

contains some dummy variables associated with public holidays and `holidayf`

contains the corresponding variables for the first 100 forecast periods, then the following code can be used:

y <- msts(x, seasonal.periods=c(7,365.25)) z <- fourier(y, K=c(2,5)) zf <- fourierf(y, K=c(2,5), h=100) fit <- auto.arima(y, xreg=cbind(z,holiday), seasonal=FALSE) fc <- forecast(fit, xreg=cbind(zf,holidayf), h=100) |

The main disadvantage of the ARIMA approach is that the seasonality is forced to be periodic, whereas a TBATS model allows for dynamic seasonality.

### Related Posts:

- hts with regressors
- Exponential smoothing and regressors
- New in forecast 6.0
- Forecasting with long seasonal periods
- The ARIMAX model muddle