Forecast reconciliation: A review

George Athanasopoulos, Rob Hyndman, Nikolaos Kourentzes and Anastasios Panagiotelis

International Journal of Forecasting, 2024, vol. 40, issue 2, 430-456

Abstract: Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography.

Keywords: Aggregation; Coherence; Cross-temporal; Hierarchical time series; Grouped time series; Temporal aggregation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Working Paper: Forecast Reconciliation: A Review (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:2:p:430-456

DOI: 10.1016/j.ijforecast.2023.10.010

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