To Implement Linear Prediction:
Choose Process > Linear Prediction.
The dialog shows three column of values. You normally only use the first column. The other fields are reserved for the rare case in which you want to reconstruct two or three non contiguous portions of the interferogram.
Enter the index of the first point to be predicted/reconstructed.
Normally points are counted starting from zero.
Bruker digital data represent an important exception, because the first index is a negative value.
Press “n” on the keyboard to see, from the scale labels, how points are numbered.
Enter the index of the last point to be predicted/reconstructed.
The third parameter (Points to Extrapolate from) is the number of equations that will solve the problem. It must be less than the number of points of the spectrum. In practice, you'll normally enter a value higher than allowed and let the program correct it automatically. Solving many equations takes time and memory, but can increase the accuracy. When there are really a lot of points in the spectrum, you may consider a lower value, for example 10-20 times the number of points to predict or 2-3 times the estimated number of signals (see below).
Enter the number of LP coefficients (Number of Signals). It corresponds, in first approximation, to the expected number of peaks into the interferogram. For example, an heteronuclear spectrum can contain hundreds of peaks, but only a few of them (or even zero) along each column. The number of coefficients should be equal, in theory, to the number of peaks into a single column. In practice, you need more coefficients to include, into the model, also the presence of noise and the inhomogeneity of the magnetic field.
Choose an algorithm. The best choice (fast and stable) is the Singular Value Decomposition. The Zhu and Bax method is stable, but only for forward prediction, and twice as slow. The Fast algorithm is the fastest one, but also the less stable.