Hello guys! I am working on a time series analysis thing that has one dimensional data series as an input and focuses mainly on spotting anomalies. I'm using nupic, but I want to have a backup plan for situations, where the data are not appropriate for the network, just to do simple analysis like detection of the most obvious outliers - ideally before learning the whole network (which would be easy as I can take a look at various metrics and draw pretty good conclusions from that). So I need a set of conditions, based purely on the dataset, to decide if nupic is usable. The question in fact lies a bit deeper - what are the necessary attributes of the data, if we want use nupic in general? I can think size of data sample, should be large enough, how about the degree of seasonality? I was thinking about the measurement of seasonality for most common patterns - like daily and weekly periods and if it's too low then dismiss the network - but maybe the HTM is able to spot something not obvious? Or do I expect too much from the algorithm?
I do realize that the whole concept of performance in the field of anomaly detection dealing with real time series is a bit hazy, but I would be really happy to hear your insights and empirical observations on the matter. Thank you! Karin
