Hi Nupic, I am trying to see if I can do anomaly detection over a data set that represents object tracks. Each object track is a list of data points that have the following information:
- timestamp - position (x,y between 0 and 1) - speed I want to learn a large number for such tracks and then look for anomalies in new tracks. This is kind of like the nupic.geospatial example but the position data is in a different coordinate system. I am looking at using a vector encoder with each sample being [x,y,speed] and then feeding each track as a separate sequence into the model. Questions: - is this the correct approach or is there some better way of encoding the data? - is it possible to swarm over this to find the best model? Thanks, Zvika
