FoReco - Forecast Reconciliation
Classical (bottom-up and top-down), optimal combination and heuristic point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and probabilistic (Girolimetto et al. 2024 <doi:10.1016/j.ijforecast.2023.10.003>) forecast reconciliation procedures for linearly constrained time series (e.g., hierarchical or grouped time series) in cross-sectional, temporal, or cross-temporal frameworks.
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forecastingreconciliationtime-series
7.56 score 39 stars 2 dependents 129 scripts 585 downloads
FoCo2 - Coherent Forecast Combination for Linearly Constrained Multiple Time Series
Methods and tools designed to improve the forecast accuracy for a linearly constrained multiple time series, while fulfilling the linear/aggregation relationships linking the components (Girolimetto and Di Fonzo, 2024 <doi:10.48550/arXiv.2412.03429>). 'FoCo2' offers multi-task forecast combination and reconciliation approaches leveraging input from multiple forecasting models or experts and ensuring that the resulting forecasts satisfy specified linear constraints. In addition, linear inequality constraints (e.g., non-negativity of the forecasts) can be imposed, if needed.
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combinationforecastingreconciliationtime-series
3.95 score 3 stars 1 scripts 551 downloads
FoRecoML - Forecast Reconciliation with Machine Learning
Nonlinear forecast reconciliation with machine learning in cross-sectional (Spiliotis et al. 2021 <doi:10.1016/j.asoc.2021.107756>), temporal, and cross-temporal (Rombouts et al. 2024 <doi:10.1016/j.ijforecast.2024.05.008>) frameworks.
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forecastingmachine-learningreconciliationtime-series
3.88 score 1 stars 1 scripts 490 downloads