What sort of fit does Beeminder perform when you enable the “Turquoise Swath”?
I’m wondering because I noticed wild variations sometimes appearing before or after the existing data points on various graphs that have it enabled. Have the bees considered using Gaussian processes to perform the fitting a la the Automatic Statistician? Depending on the kernel selected, you can provide a much stronger prior on what curves should look like, reducing those wild swings outside the existing data.
There has even been some work on compositional kernel construction (e.g. this paper, which might be useful for capturing structure like a predictable flurry of activity at the end of the month or breaks on the weekend.
I’m not entirely sure what the computational demands are for fitting something like a Gaussian process compared to what Beeminder does now but just wanted to throw the idea out there in case it sparked ideas for features down the road.