I wanted, to smooth data signal.
For example, using mean
But in specific way, that new signal (y), need to have some attributes:
for example, linear relationship
I tried other obj function with jump ipopt nlexpression, but optimization says always optimal solution found 0 in 1 step
Is there a way to compose original signal into new one containing “required atributes” ?
In R, there is function decompose, which create vector cotaining seasonality, then remainder=original vector-seasonality. Problem is, reminder has sometimes poor autocorrelation, which can leads to even more non-smooth signal, than original .
Similar, smoothing data via mean-ing sometimes results in smooth vector, but if it contains poor autocorrelation, smoothed signal is “nonstructural”
I was wondering, if it is possible to calculate/model similiar tasks in julia including customization.