Forecast Reconciliation
Forecast Reconciliation is a post-processing methodology in demand and revenue forecasting that transforms a set of incoherent base forecasts for related series—typically linked by hierarchical or grouped aggregation structures—into an adjusted set of coherent forecasts that satisfy the aggregation constraints while optimally preserving the information content of the original forecasts.
36
Publications
1.6K
Citations
60
Authors
36
Institutions
Publications per year
2017–2026
31
Children
Aggregate ConsistencyBase Forecast Bias CorrectionBayesian Forecast ReconciliationBottom-up Forecast ReconciliationCross-sectional Forecast ReconciliationError Correction In ForecastingForecast CoherenceGrouped Time Series ForecastingMachine Learning Forecast ReconciliationMiddle-out Forecast Reconciliation
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36

Monash University
Melbourne, Australia
10 h-index
21 publications
1.7K citations
Technical University of Denmark
Kongens Lyngby, Denmark
5 h-index
18 publications
533 citations

University of Auckland
Auckland, New Zealand
4 h-index
4 publications
500 citations

The University of Sydney
Sydney, Australia
4 h-index
4 publications
197 citations

University of Bath
Bath, United Kingdom
3 h-index
4 publications
220 citations

University of Padua
Padua, Italy
3 h-index
6 publications
108 citations

University of Skövde
Skövde, Sweden
2 h-index
2 publications
51 citations
National University of Singapore
Singapore, Singapore
2 h-index
4 publications
242 citations
1–8 of 36
4 publications · 175 citations
4
h-index
4 publications · 191 citations
4
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3 publications · 128 citations
3
h-index
2 publications · 45 citations
2
h-index
1 publications · 13 citations
1
h-index
1–5