The work introduces projection and UKF-based conditioning methods for reconciling probabilistic forecasts under nonlinear constraints and reports accuracy gains on synthetic and real data.
Forecasting: Principles and Practice
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.
Falcon-X introduces a latent prototype space with Unified Prototype Diff-Attention and Latent Entity Attention for heterogeneous multivariate time series forecasting.
citing papers explorer
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Nonlinear Probabilistic Forecast Reconciliation
The work introduces projection and UKF-based conditioning methods for reconciling probabilistic forecasts under nonlinear constraints and reports accuracy gains on synthetic and real data.
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Online forecast reconciliation using linear models
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.