Here is my plot using the full time series data to make the model.
Data analysis could be made in many different ways, but my hammer is Gaussian process, and so I modeled the data with a Gaussian process with six components
1) slowly changing trend
2) 7 day periodical component capturing day of week effect
3) 365.25 day periodical component capturing day of year effect
4) component to take into account the special days and interaction with weekends
5) small time scale correlating noise
6) independent Gaussian noise
- Day of the week effect has been increasing in 80′s
- Day of year effect has changed only a little during years
- 22nd to 31st December is strange time
I [Aki] will make the code available this week, but we have to first make new release of our GPstuff toolbox, as I used our development code to do this.
I have no idea what’s going on with 29 Feb; I wouldn’t see why births would be less likely on that day. Also, the above graphs are great, but I think the ideal model would have some automatic “ringing” to balance out the highs with the lows. For example, if there are fewer births on 4 Jul, you’d expect to see more on 2-3 Jul and 5-6 Jul.