# Frequency limit when applying Holt-Winters model to daily dataset

I am trying to use the Holt-Winters model to forecast the daily pollution rates of some cities. I have almost 4 years of daily data available and would like to make a prediction for the next 180 days.

For simplicity, I'll make a quick time series with rnorm() to elaborate my problem.

myts <- ts(rnorm(1461), start=2016, frequency = 365)


(from what I've read on the internet, this is how you make a daily time series with ts().)

Training dataset: traints <- window(myts, end=c(2019, 181))

I tried to fit a Holt-Winters additive model by:

fit1 <- hw(traints, seasonal='additive')


but it gave an error:

Error in ets(x, "AAA", alpha = alpha, beta = beta, gamma = gamma, phi = phi,  :
Frequency too high


When searching around it seems that ets() has a frequency limit of 24. I've read that Holt-Winters can be used for daily data so how would I work around this frequency limit to make a model(other than switching to ARIMA models)?

Rob Hyndman's suggestion is to model the seasonality using Fourier terms, and possibly using ARIMA for residuals. auto.arima() will happily do this for you.