To improve explainability, it is better to perform a real linear regression when estimating linear trends, polynomial and AR models.

PyAF uses sklearn.linear_model.Ridge model which uses a ridge parameter alpha = 1.0 by default which produces a non-zero residue mean error. Force alpha to be zero. This improves the detected cycles which are based on trend residues.

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html

The impact on the model equation is not always noticeable. Exact models are improved (when the signal is an almost linear trend).