Hi nupic-land! I will be gathering data over a long period of time from multiple sensors mounted on a common platform - GPS, sound level, light level, humidity, temperature, accelerometer - as well as gathering data from public APIs, including weather and transit conditions. So, my dataset will consist of multiple measurement streams, measured at different temporal resolutions (maybe 100-1000 Hz for accelerometer, 0.1 Hz for GPS, once a minute for transit, etc.). Each stream will have timestamps that allow me to keep them in register, so I'll know how they line up.
I want to build a system that learns the joint structure of these streams, as well as making predictions about their values in the future. I expect this structure to be rich and hierarchical. My question is: which level of the NUPIC API should I use to build this? I suspect I'll want a non-trivial region topology to capture phenomena at different levels and time scales, which makes me think I should at least use the Network level. But that's in C++, if I understand correctly, and I'd much rather use python. This would suggest the OPF, but the warnings about the current state of that code scare me - and I'd like to be close enough to the action that I can dig in and understand what's going on, rather than, say, use swarming to treat the system as more of a black box. Any suggestions appreciated. Best, Beau
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