I’m not going to string you along until the end, dear reader, and say “Didn’t achieve anything groundbreaking but thanks for reading ;)”.

This network isn’t exactly a get-rich-quick scheme yet — when I make one, I probably won’t blog about it.

It did, however, shed some interesting light on the 2018 Bitcoin crash. The car we’ve built runs, even if it often runs into walls and off cliffs. From here, it’s a question of tuning.

Last week I went through a good example of Hierarchical Temporal Memory algorithms predicting power consumption based only on the date+time & previous consumption. It went pretty well, very low mean-squared-error. HTM tech, unsurprisingly, does best when there’s a temporal element to data — some cause and effect patterns to “remember”.

So I wondered: “What’s some other hot topic with temporal data hanging around for any random lad to download?” Bitcoin.

Now hold on, is this a surmountable challenge in the first place? Surely nobody can predict the stock market, otherwise everyone would be doing it.

But then that begs the question: why is there an entire industry of people moving money around at the proper time, and how are they afloat — if not for some degree of prediction?

I’m fairly certain that crypto prices, at least, can be “learned” to some degree, because:

  1. If anyone had a magic algorithm to reliably predict the prices, they’d keep it to themselves (or lease it out proprietarily)
  2. There’s a company that already does this

Intelletic is an interesting real-world example. Their listed ‘cortical algorithms’ are based on HTM (neo_cortex_) tech, and they list “Price Prediction Alerts” as a main product for investors to make use of.

#cryptocurrency #machine-learning #data-science #artificial-intelligence #bitcoin #deep learning

Applying Temporal Memory Networks to Crypto Prediction
2.00 GEEK