I need to do seasonality analysis on a daily time series by which I mean the following:
Understand the relation between day of the week and data.
Use deseasonalized series as an input to the forecasting model.
I am comparing 2 methods for this:
Usual dummy variable
Do a regression yt= b1D1+b2D2...b5D5 + ut where bi are the regression coefficient and Di are the dummy for day of the week. ut is the residual.
Use bi coefficients as an estimate of the average seasonal effect of the day of the week and the residual series as a deseasonalized series for forecasting.
Averaging over data
- Calculate average by the day of the week over all data. Call this average Ai (so 5 total from A1 to A5).
- Divide each Ai by Average(A1 to A5). Call it Si. This gives an estimate of seasonal effect of each day.
- To deasoanlize the series, divide the original series by Si.
I am trying to understand the conceptual differences in these two approaches. Is one approach preferable to the other?