Tracking Stock Volume Anomalies With Grok

I wrote a blog post on some of my recent work at Numenta, where I’m interning this summer.

http://numenta.com/blog/detecting-anomalies-in-stock-volumes.html

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MHacks III

This weekend, I went with the Purdue Hackers club to MHacks Winter 2014 in Detroit, Michigan. This was the first major hackathon I’d been to, and it was an awesome experience.

The four person team I was a part of created a hardware and software solution for mobile ordering and checkout which we dubbed Spark. Check out our submissionhere.

We won the “Most Technically Impressive” and “Most Viable Startup” awards!

Cube, the chat bot

My most recent project (besides surviving the 2013 Summer of Math) has been writing a chat bot for the room my friends and I hang out in. It’s called Cube, and is written in python (using sleekxmpp).

Here’s a github link

Cube is a markov chain, pseudo-random text generating bot who can say some pretty ridiculous things.

A markov chain takes input text – in this case, directly from our human speech in the chat room – and ‘slices’ it up into key-value pairs, where the key is a 2-word tuple, and points to a single word value. Cube appends END tokens so we know where to start and stop generating, and stores the resulting python dict.

For example if someone were to say “The quick brown fox”, it would become:

END the -> quick
the quick -> brown
quick brown -> fox
brown fox -> END

Over time, there’s overlap in our speech, and these are added to the dict. For instance, if someone were to then say “The quick brown cow”, our updated dict would have

END the -> quick
the quick -> brown
quick brown -> {fox, cow}
brown fox -> END
brown cow -> END

When prompted to generate a sentence, we starts a markov chain with a random key where the first token is END, and follow it until we encounter another END token, two words at a time. If there are multiple values for a given key tuple, we choose at random. Each new word is appended to the list of words that will comprise our bot-generated text.

It’s worth mentioning at this point that the probability of a given next word is stored to the dict as well. For instance if we had “I am fat”, “I am fat”, and “I am hungry”, the values stored would be:

I am -> {fat, fat, hungry}

So there would be a 66% chance of selecting “fat”, and a 33% chance of selecting hungry.

That’s the basic idea behind markov chains. Cube however, does this slightly differently.

When a new sentence is input, Cube actually saves two markov chains. One forwards (like we’ve covered), and one in reverse. So the sentence “the quick brown fox” becomes the forward dict from above, as well as:

END fox -> brown
fox brown -> quick
brown quick -> the
quick the -> END

These two markov chains, forward and reverse, are stored in two separate dicts, which makes this whole model a little trickier to visualize, but bear with me. Now, when we go to generate a sentence, we can start from any word in the corpus and run a markov chain in both directions, until each reaches an END token. This allows for much greater variance in generated text, because there are obviously many more words in the corpus than there are words-preceded-by-END-tokens.

Over time, Cube starts to say some very interesting things indeed.

viraj: cube
cube: the agent started taking out his badge and had anyone used the criticisms box to agree with your fingers