I have some time series data that I would like to approximate with a twice-differentiable function. Each time series has ~10,000 datapoints, so I definitely do not want a function that passes through all these points. This rules out using
I have no mathematical model for these data at all, otherwise I would use
NonlinearModelFit for this.
The only solution I can think of would be to perform some type of smoothing on the data (
LowpassFilter?), and then somehow generate an approximating function object from the smoothed data.
Is there a built-in function for doing this sort of thing? (It's not that I think there's anything wrong with the recipe outlined above, but as a rule I prefer to use built-in methods over home-spun ones whenever possible.)