# basic time series seasonality question

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:

1. 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.

2. 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?

• when u have found your answer accept the one you like to close the question. – IrishStat Dec 19 '16 at 17:52
• You may take inspiration on how it is done in existing tools (e.g. statsmodels) – Manu H Jun 18 '19 at 8:33