I have a customer who is attempting to user FDE to determine if their data from censors measured over time is "typical". They want to use FDE to create FPCs and have these FPCs be used to judge if a run is typical and flag a run that is non-typical by creating specs for the FPCs. They don't want the model to update with new data just to analyze the data with the current model and create FPCs for the new data. This seems to work really well by having the new data be validation data, but the issue is the model has to "re-run" on the old data, which takes quite a bit of computing power and time since the model that worked really well for them was a p-sline model with 400 nodes.
Does anyone know of a way to save the spline formula for the functional principal components without having to re-run the model through FDE everytime?
Along those same lines is there a way to save the data that has gone through the cleaning steps, aligning and transforming steps?
One other word of warning I would not recommend running p-splines in a live demo on a descent sized data set. Took about 15 minutes then crashed JMP when the customer was looking at ~500,000 rows of data on a 16GB machine.