# Hodrick–Prescott filter on train/test split

I have a multivariate time series $$X$$ which has been time-based split to $$X_{train}$$ and $$X_{test}$$. If I wanted to do standard scaling without data leak, it is possible to learn the scaling parameters on $$X_{train}$$ and apply the same to $$X_{test}$$

X_train_scaled = StandardScaler.fit_transform(X_train)
X_test_scaled = StandardScaler.transform(X_test)


Similarly, is it possible to apply the Hodrick-Prescott filter without creating data leakage in regards to $$X_{train}$$ and $$X_{test}$$.? I am asking about both the mathematical and technical feasibility.

I assume that a working solution could exploit the current statmodels implementation of HP filter, but unfortunately there is no API resembling fit_transform / transform.