FoRecoPy: Forecast Reconciliation in Python

Forecast reconciliation is a post-forecasting process aimed at improving the accuracy and coherence of forecasts for a system of linearly constrained time series (e.g., hierarchical, grouped, or temporal structures).

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.

Future versions will expand the scope to include the cross-temporal framework, non-negative constraints and probabilistic reconciliation.

Installation

Make sure to have a working JAX installation (please, follow these instructions).

To install the package from PyPI, call:

pip install forecopy

To install the latest GitHub , just call the following on the command line:

pip install git+https://github.com/danigiro/FoRecoPy@<RELEASE>

Features

• Optimal combination reconciliation via projection and structural approaches
• Tools for both cross-sectional (csrec) and temporal (terec) reconciliation
• Different covariance matrix approximation
• Support for custom aggregation or constraints matrices
• Option to enforce non-negativity on reconciled forecasts
• Efficient solvers suitable for high-dimensional problems

Quick Examples

Examples of cross-sectional and temporal forecast reconciliation are available here

License

MIT License. See LICENSE for details.