As a side hobby, I have been exploring forecasting time series (in particular, using R).
For my data, I have the number of visits per day, for every day going back almost 4 years. In this data there are some distinct patterns:
- Monday-Fri has a lot of visits (highest on Mon/Tue), but drastically less on Sat-Sun.
- Certain times of the year drop (i.e. many less visits around U.S. Holidays, summers show less growth)
- Significant growth year-to-year
It would be nice to be able to forecast an upcoming year with this data, and also use it to have seasonally adjusted month-to-month growth. The main thing that throws me off with a monthly view is:
- Certain months will have more Mon/Tue than other months (and that isn't consistent over years either). Therefore a month that happens to more weekdays needs to be adjusted accordingly.
Exploring weeks also seems difficult since the week numbering systems change from 52-53 depending on the year, and it seems
ts doesn't handle that.
I'm pondering taking an average for the weekdays of the month, but the resulting unit is a bit strange (Growth in Avg. Weekday Visits) and that would be dropping data which is valid.
I feel this sort of data would be common in time series, (say for example electricity usage in office building might be something like this), anyone have any advice on how to model it, in particular, in R?
The data I am working with is pretty straight forward, it starts like:
[,1] 2008-10-05 17607 2008-10-06 36368 2008-10-07 40250 2008-10-08 39631 2008-10-09 40870 2008-10-10 35706 2008-10-11 18245 2008-10-12 23528 2008-10-13 48077 2008-10-14 48500 2008-10-15 49017 2008-10-16 50733 2008-10-17 46909 2008-10-18 22467
and continues like this up to the present, with an overall trend of growth, some dips around US holiday weeks, and growth generally slowing during the summer.