We are two students who are trying out some different models for an anomaly detection project.
Our data is timeseries from 40 different sensors, where the values have a close relation to each other, but does not have a close relation to the timestamps. The frequency is one measurement from each sensor each second.
We have tried running the nupic examples on our data without changing too much on the parameters for a singe series, and it seems to spot some anomalies.
The idea we have, is to use the data from all the 40 sensors to spot anomalies in one sensor.
Would HTM be a good choice for this problem or should we go for some LSTM or OCSVM model instead?
If so, how should the data from the 40sensors be encoded to predict only one of the sensor values?
And how memory/compute intensive would such a model be?
Thanks for any help!