I’m working on an AI project and I want to use HTM to make predictions on datastreams within this project.
I have some continuous (must be processed in realtime) time series data. These data streams don’t fit into any predefined typical data types such as ‘audio data’ or ‘price data’ or ‘language data’. They’re just data streams of bit/binary patterns. and they’re sparse. So they’re basically SDR’s, they even have some very loose semantic meaning to each bit. But the data stream could technically represent anything.
Anyway, so I’m looking for a generalizable time-series-pattern-prediction algorithm. I don’t need the hierarchy stuff. I just want better than chance predictions on successive patterns. That is to say, I want a basic spatiotemporal prediction algorithm that I can plug into the system.
When I’ve asked what the most generic solution would be on other forms I get data scientists asserting “There is no general algorithm, you must tell us your exact use case!” Then they get all hung up on what type of data it is, yadda, yadda yadda. It’s annoying. I just want to predict the patterns, guys, let’s not overcomplicate it.
So, anyway, the question I have is, since I basically know the HTM algorithm, and I’m working in python, how do I use the Nupic library in python to just take advantage of the basic (what used to be called CLI) algorithm for time-series pattern prediction? Is the code base mature enough for me to do that? Is it modular enough? If so where do I get started?