Thank you for all your efforts so far! These have been enjoyable and instructive. As expected, I enjoyed this one. :D
I was a little worried about Doom, but not enough, it seems. And I think it was entirely fair to make Doom work as it does. The thematic naming of the rest is a plenty suggestive clue.
Let me jot down what I remember learning:
the autocorrelation coefficient of random walks tend to hover around 0.42
it’s hard to get large coefficients accidentally, so tossing tons of series together and asking for any pairs that see nontrivial correlation is a decent discovery method
once again, if fitting parameters to a sum of two functions both of which have a constant term, remove one of the terms so you don’t get confused by huge opposite values :D
once again, pairwise scatterplots are super helpful for just eyeballing obvious patterns
sums of sines overfit just as well as polynomials, in their domain. I knew this only in my head, not viscerally until now
Thank you for all your efforts so far! These have been enjoyable and instructive. As expected, I enjoyed this one. :D
I was a little worried about Doom, but not enough, it seems. And I think it was entirely fair to make Doom work as it does. The thematic naming of the rest is a plenty suggestive clue.
Let me jot down what I remember learning:
the autocorrelation coefficient of random walks tend to hover around 0.42
it’s hard to get large coefficients accidentally, so tossing tons of series together and asking for any pairs that see nontrivial correlation is a decent discovery method
once again, if fitting parameters to a sum of two functions both of which have a constant term, remove one of the terms so you don’t get confused by huge opposite values :D
once again, pairwise scatterplots are super helpful for just eyeballing obvious patterns
sums of sines overfit just as well as polynomials, in their domain. I knew this only in my head, not viscerally until now