# Decomposition of daily time series (several years) with multiple seasonal patterns

i have a daily time series of several years. Graph & CSV-file

So far i could figure out with an based on an acf graph and this method:

timeSeriesObj = ts(x,start=c(1999,1,1),frequency=7)
fit <- tbats(timeSeriesObj)
seasonal <- !is.null(fit$seasonal) seasonal returns: TRUE timeSeriesObj = ts(x,start=c(1999,1,1),frequency=365.25) fit <- tbats(timeSeriesObj) seasonal <- !is.null(fit$seasonal)
seasonal

returns: TRUE

that i have a weekly as well as an annual seasonality.

How do i look for monthly seasonality? Is it a legit way to sum up all days of a month so i get 12 months a year and then check the acf graph again?

My final goal would be to estimate the different seasonal factors and remove them from the data in order to analyse the effects of different dates as for example easter or 4th of july.

• With daily data it makes a sense to try daily time series. You may apply multiplicative seasonality in ARIMA with lag 20 (business) or 30 (calendar) days. You can also add seasonal dummies. – Aksakal Jul 15 '15 at 13:51