These statements contradict each other, don’t they?
If there is one process, and you have lots of data representing this process occurring over and over, this is a good thing to train an HTM model. The model will learn the structure of the temporal process, and as you give it new process data, it should be able to return useful anomaly indications. But you must reset the temporal memory algorithm at the end of each process.
If you have many different processes that each need to be learned, you need to train one HTM model on each process.