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.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A neural reconcilier produces coherent station and OD demand forecasts for urban rail transit and reduces OD error by up to 17.45 percent under multi-step disruption scenarios.
citing papers explorer
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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.
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Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions
A neural reconcilier produces coherent station and OD demand forecasts for urban rail transit and reduces OD error by up to 17.45 percent under multi-step disruption scenarios.