arXiv 25 May 2023 · Statistics — Applications · publishedInternational Journal of Forecasting (2022) · 4 citations (OpenAlex)
arXiv:2305.16255 · PDF · DOI · OpenAlex · Extracted main text
Aggregated curves are common structures in economics and finance, and the most prominent examples are supply and demand curves. In this study, we exploit the fact that all aggregated curves have an intrinsic hierarchical structure, and thus hierarchical reconciliation methods can be used to improve the forecast accuracy. We provide an in-depth theory on how aggregated curves can be constructed or deconstructed, and conclude that these methods are equivalent under weak assumptions. We consider multiple reconciliation methods for aggregated curves, including previously established bottom-up, top-down, and linear optimal reconciliation approaches. We also present a new benchmark reconciliation method called 'aggregated-down' with similar complexity to bottom-up and top-down approaches, but it tends to provide better accuracy in this setup. We conducted an empirical forecasting study on the German day-ahead power auction market by predicting the demand and supply curves, where their equilibrium determines the electricity price for the next day. Our results demonstrate that hierarchical reconciliation methods can be used to improve the forecasting accuracy of aggregated curves.
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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 | F. Ziel and R. Steinert (2016) Electricity price forecasting using sale and purchase curves: The x-model | 1.000 | 12 | 4 | 100% |
| 2 | S. L. Wickramasuriya, G. Athanasopoulos, and R. J. Hyndman (2018) Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization | 1.000 | 6 | 3 | 100% |
| 3 | R. J. Hyndman and G. Athanasopoulos (2018) Forecasting: principles and practice | 0.874 | 7 | 2 | 100% |
| 4 | F. Ziel and R. Steinert (2018) Probabilistic mid-and long-term electricity price forecasting | 0.843 | 3 | 3 | 100% |
| 5 | E. Spiliotis, M. Abolghasemi, R. J. Hyndman, F. Petropoulos, and V.… (2021) Hierarchical forecast reconciliation with machine learning | 0.737 | 3 | 2 | 100% |
| 6 | T. Di Fonzo and D. Girolimetto (2021) Forecast combination based forecast reconciliation: insights and extensions | 0.644 | 2 | 2 | 100% |
| 7 | S. Haben, J. Caudron, and J. Verma (2021) Probabilistic day-ahead wholesale price forecast: A case study in great britain | 0.644 | 2 | 2 | 100% |
| 8 | S. Kulakov (2020) X-model: Further development and possible modifications | 0.644 | 2 | 2 | 100% |
| 9 | M. Soloviova and T. Vargiolu (2021) Efficient representation of supply and demand curves on day-ahead electricity markets | 0.644 | 2 | 2 | 100% |
| 10 | J. Friedman, T. Hastie, and R. Tibshirani (2008) Sparse inverse covariance estimation with the graphical lasso | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Stealing accuracy: Predicting day-ahead electricity prices with temporal hierarchy forecasting (THieF) | 0.405 | 1 | 1 |