I am working on a time series that contains daily sales data over 2 and a half years. The aim of the project is to estimate the impact of marketing expenditure on the sales, while accounting for seasonality and trend.
A plot of the data reveals a strong weekly seasonality. There also seems to be yearly seasonality. However, as there is only 2 and a half years of data available, I was wondering what the best method(s) would be to take the yearly seasonality into account without sacrificing too much of the data for testing the impact of marketing expenditure on?
I am more confortable in Python, but if need be I can use R as well.