Hierarchical Forecasting

Hierarchical forecasting addresses the challenge of accurately predicting multiple time series organized in a hierarchical structure, ensuring consistency between aggregate and disaggregate forecasts. Current research emphasizes developing sophisticated models, including graph neural networks and hierarchical attention mechanisms, to capture complex interdependencies within the hierarchy and improve forecast accuracy and coherence. These advancements are crucial for various applications, such as resource allocation in cloud computing, supply chain management, and renewable energy forecasting, where accurate and consistent predictions across different levels of aggregation are essential for effective decision-making. The field is also seeing increased focus on probabilistic forecasting and efficient algorithms that scale to handle millions of time series.

Papers

November 10, 2024

Local vs. Global Models for Hierarchical Forecasting
Zhao Yingjie, Mahdi Abolghasemi
State of the Art Forecasting Global Model Global Forecasting Hierarchical Forecasting Hierarchical Time Series Forecasting

August 2, 2024

Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making
Yang Luo, Shiyu Wang, Zhemeng Yu, Wei Lu, Xiaofeng Gao, Lintao Ma, Guihai Chen
Resource Allocation Large Scale Datasets Large Scale Model Scaling Method Hierarchical Forecasting Bayesian Decision Resource Scaling Hierarchy Aware Attention

May 29, 2024

DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir
Graph Neural Network Study Feature Multivariate Time Series Forecasting Method Hierarchical Forecasting Hierarchical Time Series Hierarchical Time Series Forecasting

February 22, 2024

Multivariate Online Linear Regression for Hierarchical Forecasting
Massil Hihat, Guillaume Garrigos, Adeline Fermanian, Simon Bussy
Logarithmic Regret Hierarchical Forecasting Lloyd Algorithm Online Linear Regression

October 19, 2023

Hierarchical Forecasting at Scale
Olivier Sprangers, Wander Wadman, Sebastian Schelter, Maarten de Rijke
Visual Analogue Scale Univariate Time Series Forecasting Hierarchical Forecasting Hierarchical Loss

August 7, 2023

Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure
Lucas English, Mahdi Abolghasemi
Forecasting Performance Wind Power Hierarchical Structure Temporal Structure Hierarchical Forecasting Wind Speed Forecasting

July 19, 2023

Probabilistic Forecasting with Coherent Aggregation
Kin G. Olivares, Geoffrey Négiar, Ruijun Ma, O. Nangba Meetei, Mengfei Cao, Michael W. Mahoney
Probabilistic Forecast Factor Model Multivariate Forecasting Hierarchical Forecasting Consistent Aggregation Coherent Forecast

June 21, 2023

Hierarchical Neural Simulation-Based Inference Over Event Ensembles
Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer
Neural Network Simulation Based Inference Neural Estimator Hierarchical Forecasting Global Posterior

May 30, 2023

Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Andrea Cini, Danilo Mandic, Cesare Alippi
Inductive Bias Graph Pooling Pyramid Network Hierarchical Forecasting Hierarchical Time Series Temporal Graph Clustering Hierarchical Regional Bias

May 25, 2023

Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions
Paul Ghelasi, Florian Ziel
Application Proficiency Aggregated Curve Day Ahead Hierarchical Forecasting Probabilistic Reconciliation

May 22, 2023

Hierarchical Partitioning Forecaster
Christopher Mattern
Precipitation Nowcasting Sequential Prediction Hierarchical Forecasting Linear Forecasting

February 11, 2023

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, Shiyu Wang, James Zhang, Xinxin Zhu, Xuanwei Hu, Yunhua Hu, Yangfei Zheng, Lei Lei, Yun Hu
Time Series Forecasting Multivariate Time Series Forecasting Part Whole Hierarchy Forecasting Performance Hierarchical Information Hierarchical Forecasting Hierarchical Time Series Learning Structure Single Population Optimization Task

February 8, 2023

Structural hierarchical learning for energy networks
Julien Leprince, Waqas Khan, Henrik Madsen, Jan Kloppenborg Møller, Wim Zeiler
Human Prediction Hierarchical Learning Coherence Modeling Hierarchical Forecasting

January 30, 2023

Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads
Julien Leprince, Henrik Madsen, Jan Kloppenborg Møller, Wim Zeiler
Hierarchical Learning Hierarchical Forecasting Multi Channel Time Series Coherent Forecast

November 28, 2022

Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami
Time Series Forecasting Hyperparameter Optimization Related Hyperparameters Hierarchical Forecasting Forecasting Competition

July 7, 2022

HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
Kin G. Olivares, Federico Garza, David Luo, Cristian Challú, Max Mergenthaler, Souhaib Ben Taieb, Shanika L. Wickramasuriya, Artur Dubrawski
Baseline Model Hierarchical Forecasting Reference Model Learning Based Forecasting Forecast Aggregation

April 21, 2022

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting
Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen
Deep Probabilistic Model Hierarchical Forecasting Dirichlet Process Mixture Model Coherent Forecast