Hierarchicalforecast

Abstract

1. Introduction

Hierarchical Forecast Probabilistic hierarchical forecasting with statistical and econometric methods

A vast number of time series datasets are organized into structures with different levels or hierarchies of aggregation. Examples include cross-sectional aggregations such as categories, brands, or geographical groupings, or temporal aggregations such as weeks, months or years. Coherent forecasts across levels are necessary for consistent decision-making and planning. Hierarchical Forecast offers different reconciliation methods that render coherent forecasts across cross-sectional and temporal hierarchies.

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2. Objective

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

This project demonstrates how Python can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Classic reconciliation methods:
• BottomUp: Simple addition to the upper levels.
• TopDown: Distributes the top levels forecasts trough the hierarchies.
• Alternative reconciliation methods:
• MiddleOut: It anchors the base predictions in a middle level. The levels above the base predictions use the bottom-up approach, while the levels below use a top-down.
• MinTrace: Minimizes the total forecast variance of the space of coherent forecasts, with the Minimum Trace reconciliation.
• ERM: Optimizes the reconciliation matrix minimizing an L1 regularized objective.
• Probabilistic coherent methods:
• Normality: Uses MinTrace variance-covariance closed form matrix under a normality assumption.
• Bootstrap: Generates distribution of hierarchically reconciled predictions using Gamakumara's bootstrap approach.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Nixtla/hierarchicalforecast.git
cd hierarchicalforecast

uv pip install hierarchicalforecast

pip install hierarchicalforecast

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by Nixtla and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.