I guess I never responded to this initially. Sorry!
It depends on your model params. If you are predicting 1 step ahead, the prediction will be for one minute in the future. If you’re predicting 5 steps ahead, 5 minutes in the future. This is only valid if all the previous data intervals have been at 1 minute.
It is trying to predict both time and scalar value, but it won’t be good because there was just an abnormal interval. See this topic for more info:
I would not reset in this case. A reset is like manually telling the algorithm that a sequence just ended, and it should sever any current segments that are trying to learn the next point. It ends the sequence. You would only do this if there were a cycle you could identify in the input that was not already described by time itself. What I mean is you don’t have to reset for end of day, or hourly, because those semantics are already a part of the encoding.
You might want to reset if the input data is associated with a machine that just went through a cycle, and was starting again. More info on this here.
My suggestion around aggregation has always been to do it manually, not rely on the swarming process to do it for you. You’ll have better success this way. How you aggregate depends greatly on the structure of your data. The main rule is to chart the aggregated data and see if you, as a human, can see patterns in the chart. If so, NUPIC will be more successful. If you cannot find any patterns, maybe there are none to find at that aggregation level.