arXiv (All)
open access
Coherent Hierarchical Forecasting for Proportion and Discrete Time Series
Hierarchical and grouped time series arise when a multivariate time series is forced to satisfy a set of aggregation constraints, motivating forecast reconciliation methods that ensure coherent forecasts of such hierarchical structures. Many real-world applications involve discrete or bounded suppor
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting#784890
- Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting#783300
- A Forecast Combination Framework for Hierarchical and Grouped Time Series Reconciliation#615215
- A Forecast Combination Framework for Hierarchical and Grouped Time Series Reconciliation#614248
- When Does Forecasting Reveal Temporal Structure? A Stability Analysis of Time-Series Structural Selection#652444
Record · ID 1011632
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive