FoRecoPy: Forecast Reconciliation in Python
Forecast Reconciliation is a a post-forecasting process aimed to improve the accuracy and align forecasts for a system of linearly constrained (e.g. hierarchical/grouped) time series.
The FoRecoPy package is inspired by the R package FoReco and brings similar functionality
to Python. It is designed for researchers, practitioners, and data scientists
who use Python for time series forecasting and want access to state-of-the-art
reconciliation methods.
Currently, FoRecoPy supports:
Regression-based reconciliation (e.g. minimum trace)
Both cross-sectional reconciliation (hierarchical, grouped and linearly constrained time series) and temporal reconciliation (multiple aggregation frequencies)
Future versions will expand the scope to include the cross-temporal framework, non-negative constraints and probabilistic reconciliation.