I like sticking stuff on Hilbert curves as much as the next person, but I don’t see how it makes this particular data easier to understand. It looks like you could do the same thing in as little total space with overlapping line graphs, for example. Am I missing something here because I’m not a quant?
I don't think the point is to help 'understand' the data better, so much as to help you find similar series, which is admittedly a pretty narrow goal. That said, I posted it because I hadn't seen this tried, and it was creative, not because I think it's the best way to do this. Honestly, I think the most compact way to really approach this problem would be to make a covariance matrix of all the data and display it as a heatmap, which is probably easier too -- but this is also cool, and a bit surprising.
Oh, I’m not begrudging your posting it; for one thing, it’s got me thinking about Hilbert (and Z-order) curves again. It just seems like an odd application in an area where, I gather, more sophisticated correlation tools are the norm.
Thanks. I 'blind-spotted' the xkcd URL in your comment, and was going to add it. In the process, I hit the blog post ( http://blog.xkcd.com/2006/12/11/the-map-of-the-internet/ ) about it - interestingly, it was a rediscovery ...
The 'young fruit salad' tag, for the RGB cube, does take the cake, though ;-)
P.S. If memory serves, there was a neat Python turtle Tk graphics demo which slowly traced a colored Hilbert curve. Perhaps it's the 'fractaldemo' in http://docs.python.org/library/turtle.html
