I am currenly analysing a dataset of about 15 000 samples, each sample is made of 50 floating point values which represent moments (timestamps) when a certain event is happening. We don’t care about the event itself it’s always the same. So what really matters here are the timestamps.
The 50 values typically ranges from 0.001 second to 45 sec and they are always in growing order (it’s time passing). For each sample I also have the corresponding state of the system: it’s either 0 or 1. This is what needs to be predicted.
I have already read the various messages about having multiple values as inputs to HTM (here the 50 timestamps) so I’m pretty confortable on how to do that. I have 2 additional questions though:
Is the sparse representation going to gracefully handle those timestamp values that roughly span 4 orders of magnitude or do I need to apply some kind of pre-processing ? (like putting them on a log scale)
Second how shall I use HTM to predict the 0 or 1 state. It’s literally a binary classification problem and I was wondering how to handle this with HTM ? It turns out that state 1 samples are globally 4 times less frequent than state 0 samples, so could those sample 1 be considered as “anomalies”? Anyother approach you can think of would be appreciated.
Thanks a lot for your help.