Thanks for your input, I really appreciate it, but I'm not sure I really agree. For example, I was using a function polyfit in MatLab before, and I did indeed just tweak my code by using a first order parameter into the polyfit (which is really just a linear model) and my prediction error...
I've been playing around with different methods, and I am starting to realize for the prediction, maybe I really want a linear fit because that type of anomaly if rejected simply makes the data fit to a nice line. I may still want to store the smoothed result for evaluating other trends in the...
@IRstuff Yes, that's what I'm trying to do, but by tweaking the parameters to the trending function(or the trending method). With the sensors I have, technically all the data is true (or not false data), but I am building this trend to create a predicted output. I just don't want my system to...
@IRstuff Ah, yes...so, that's a good point. That "spike" when I generated that fake data didn't include the noise the other data did, though I'm not sure it would make much of a difference when running it through the trend. So, to answer your question, the smoothness of the spike is not...
I have a system here that takes in some data, then needs to decide what to do depending on the data, but no real data yet(...all the pieces clunking along before refining). I was hoping to get some guidance on handling the data.
So, no filters here, just creating a smoothed version (or trend)...
