For KF1 and KF2, do you change the length of the moving average or measurement noise R to get different results or both? How do you fit differeces between 2 KFs in a percentage distribution without introducing future data? Thanks!
For KF1 and KF2, do you change the length of the moving average or measurement noise R to get different results or both? How do you fit differeces between 2 KFs in a percentage distribution without introducing future data? Thanks!
The only difference between both KFs is the parameter R.
The 2nd question is a great one. When applying the rank function in Python (what computes the percentile rank), you should apply it only to the past values (rolling or expanding). If you are curious, though, you will see that the 3 scenarios (rolling, expanding, or applying at once with all values) will produce almost exact same results :)
For KF1 and KF2, do you change the length of the moving average or measurement noise R to get different results or both? How do you fit differeces between 2 KFs in a percentage distribution without introducing future data? Thanks!
The only difference between both KFs is the parameter R.
The 2nd question is a great one. When applying the rank function in Python (what computes the percentile rank), you should apply it only to the past values (rolling or expanding). If you are curious, though, you will see that the 3 scenarios (rolling, expanding, or applying at once with all values) will produce almost exact same results :)
Was the choice of ma length and parameter R, Q also optimized/fitted using test data?