Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis, Feng Li
arXiv 20 Apr 2022 · Statistics — Methodology · publishedEuropean Journal of Operational Research (2022) · 17 citations (OpenAlex)
arXiv:2204.09231 · PDF · DOI · OpenAlex · Extracted main text
The practical importance of coherent forecasts in hierarchical forecasting has inspired many studies on forecast reconciliation. Under this approach, so-called base forecasts are produced for every series in the hierarchy and are subsequently adjusted to be coherent in a second reconciliation step. Reconciliation methods have been shown to improve forecast accuracy, but will, in general, adjust the base forecast of every series. However, in an operational context, it is sometimes necessary or beneficial to keep forecasts of some variables unchanged after forecast reconciliation. In this paper, we formulate reconciliation methodology that keeps forecasts of a pre-specified subset of variables unchanged or "immutable". In contrast to existing approaches, these immutable forecasts need not all come from the same level of a hierarchy, and our method can also be applied to grouped hierarchies. We prove that our approach preserves unbiasedness in base forecasts. Our method can also account for correlations between base forecasting errors and ensure non-negativity of forecasts. We also perform empirical experiments, including an application to sales of a large scale online retailer, to assess the impacts of our proposed methodology.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wickramasuriya, Athanasopoulos \ Hyndman (2019) `Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization', Journal of the American S… | 1.000 | 8 | 4 | 100% |
| 2 | Athanasopoulos, Hyndman, Kourentzes \ Petropoulos (2017) `Forecasting with temporal hierarchies', European Journal of Operational Research 262(1), 60–74 | 1.000 | 5 | 3 | 100% |
| 3 | Wickramasuriya, Turlach \ Hyndman (2020) `Optimal non-negative forecast reconciliation', Statistics and Computing 30(5), 1167–1182 | 0.928 | 4 | 3 | 100% |
| 4 | Hollyman, Petropoulos \ Tipping (2021) `Understanding forecast reconciliation', European Journal of Operational Research 294(1), 149–160 | 0.874 | 7 | 2 | 100% |
| 5 | Di Fonzo \ Girolimetto (2021) `Forecast combination based forecast reconciliation: Insights and extensions', arXiv:2106.05653 [stat] | 0.874 | 5 | 2 | 100% |
| 6 | van Erven \ Cugliari (2015) Game-theoretically optimal reconciliation of contemporaneous hierarchical time series forecasts, in A. Antoniadis, J.-M | 0.843 | 3 | 3 | 100% |
| 7 | Panagiotelis, Athanasopoulos, Gamakumara \ Hyndman (2021) `Forecast reconciliation: A geometric view with new insights on bias correction', International Journal of Forecasting 37(1), 34… self | 0.737 | 3 | 3 | 67% |
| 8 | Hyndman, Ahmed, Athanasopoulos \ Shang (2011) `Optimal combination forecasts for hierarchical time series', Computational Statistics Data Analysis 55(9), 2579–2589 | 0.644 | 2 | 2 | 100% |
| 9 | Nystrup, Lindström, Mller \ Madsen (2021) `Dimensionality reduction in forecasting with temporal hierarchies', International Journal of Forecasting 37(3), 1127–1146 | 0.644 | 2 | 2 | 100% |
| 10 | Nystrup, Lindström, Pinson \ Madsen (2020) `Temporal hierarchies with autocorrelation for load forecasting', European Journal of Operational Research 280(3), 876–888 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 24 scored citations.