>>3I read the paper and they are pretty interesting. I didn't have know how those methods worked in such detail.
But I'm still lost when it comes to which method I should use in each situation.
For example, let's say we have a time series with random data. We don't know the probability distribution of this data or if it should form a line, sine, tangent, polynomial or something else.
It's just a bunch of (x,y) values for which we don't know the behavior. But we want to fit a curve to it so we can -
I. get a visual idea of the behavior of the data
II. extrapolate data (to predict how the data will behave in the future).
My question is which method should be used. Since we have no idea of the probability distribution it must be a non-parametric method. But that's as far as I figured out. There are many non-parametric methods.
>>5I don't know what you mean by that. Can you be a bit more specific?