Conditional model using function tslm in R package forecast

Question

I would like to use tslm with data that has intraday seasonality and a different pattern on business days and on non-business days. If data.ts is my time series then I would like to use something like

tslm(data.ts~season|businesss.dummy)

Thus I want to model season given that the dummy for this hour is True or False. I don't want

tslm(data.ts~season + businesss.dummy)

as this would just give a parallel shift on business days. I know that I can subset the data before applying the model and thus get business day data and non-business day data only but can I achieve this aim more elegantly using the right formula in tslm? Thanks!

Answer 1

You can do this by setting up the seasonal factors yourself. I'm assuming you have hourly data over three weeks, and that each week has 7 days.

x <- ts(rnorm(21*24),f=24)
dow <- rep(rep(1:7,rep(24,7)),3)
business.dummy <- (dow<=5)
seasons <- cycle(x)
seasons[!business.dummy] <- seasons[!business.dummy] + 24
seasons <- factor(seasons,levels=1:48,
    labels=c(paste("Week",1:24),paste("Weekend",1:24)))
fit <- tslm( x ~ seasons - 1)

The seasons factor has 48 levels, the first 24 corresponding to weekday hours, and the second 24 corresponding to weekend hours. You can generalize to allow other non-business days by setting the relevant values of business.dummy to FALSE.