After blogging break caused by writing research papers, I managed to secure time to write something new about time series forecasting. This time I want to share with you my experiences with seasonal-trend time series forecasting using simple regression trees. Classification and regression tree (or decision tree) is broadly used machine learning method for modeling. They are favorite because of these factors:

So you know the Cheshire cat, yeah?  Ever wonder what it’s point is?  Like, it turns up in a number of places, but does it foreshadow or emphasize plot turns?  Might it signify coming improvements or declines?

Van der Waerden’s theorem is a theorem in the branch of mathematics called Ramsey theory.

Hacker News discussion: link

See the Hacker News Discussion for additional context.

A few months ago I posted some results from experiments with highresolution GAN-generated faces. By popular request here is a little more on the approach taken and some newer results.

I’m taking my qualifying exam this Tuesday—which may surprise some of you that I haven’t already done it! This is mostly due to logistical kerfuffles as I transferred Ph.D.’s and I also tend to avoid coursework like the plague.

For my first act, two topics related to sequential data that should come up if you search google for “Cinderella science”, which you might do if you’re ever looking for stories of uncredited hard work that ultimately worked out (if only with the help of a fairy godmother).

[These are the notes from a talk I gave at the seminar]