{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"Reconciles hierarchical time-series forecasts across multiple levels of aggregation using statistical and probabilistic methods to ensure coherent predictions.","skillfed_tags":["time-series-forecasting","hierarchical-reconciliation","statistical-methods"],"use_cases":["Reconcile sales forecasts across product hierarchies (brand \u2192 category \u2192 SKU) to ensure consistency in inventory planning.","Enforce temporal coherence in forecasts across different time aggregations (daily \u2192 weekly \u2192 monthly) for supply-chain alignment.","Generate probabilistic coherent forecasts with valid prediction intervals using Bootstrap or Normality methods for risk assessment.","Combine independent forecasts from multiple models at different hierarchy levels and reconcile them into a coherent forecast.","Evaluate and benchmark reconciliation methods on hierarchical datasets using built-in evaluation metrics."],"what_it_does":"HierarchicalForecast provides reconciliation methods for time-series forecasts organized in hierarchical structures\u2014such as geographical regions, product categories, or temporal aggregations (weeks, months, years). It takes base forecasts and enforces coherence across hierarchy levels so that predictions at different aggregation levels remain mathematically consistent. The package includes classic methods like BottomUp and TopDown, alternative approaches like MinTrace and MiddleOut, and probabilistic methods such as Normality, Bootstrap, and PERMBU that generate coherent prediction intervals.\n\nThe library is designed for practitioners who need coherent forecasts across multiple organizational or temporal levels for consistent decision-making. It integrates with the broader forecasting ecosystem and provides evaluation tools to measure reconciliation quality. The package depends on standard scientific Python libraries (numpy, pandas, scikit-learn, matplotlib) and specialized tools like narwhals for dataframe abstraction and qpsolvers for optimization.","worth_installing":"Yes. Active maintenance, permissive license, and no known vulnerabilities make it a reliable choice. Medium install friction is acceptable given the specialized nature of hierarchical forecasting. Install if you need to enforce coherence across hierarchical time-series forecasts; skip if your forecasts are flat or non-hierarchical."},"id":"hierarchicalforecast","links":{"html":"https://skillfed.io/packages/hierarchicalforecast","md":"https://skillfed.io/packages/hierarchicalforecast.md","pypi":"https://pypi.org/project/hierarchicalforecast/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-04","license_spdx":null,"license_treatment":"permissive","name":"hierarchicalforecast","python_support":"supports_current","summary":"Hierarchical Methods Time Series Forecasting"},"popularity":{"monthly_downloads":318751,"position":7649,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.1"}