Guest Editors: George Athanasopoulosa, Rob J Hyndmana, Anastasios Panagiotelisb and Nikolaos Kourentzesc
a Department of Econometrics and Business Statistics, Monash University, Australia; b Discipline of Business Analytics, University of Sydney, Australia; c Skövde Artificial Intelligence Lab, Skövde University, Sweden.
Organisations make multiple decisions informed by forecasts both to plan and function efficiently. Such decisions may differ in scope, from operational, to tactical, to strategic; corresponding to different time scales from short-term to medium-term to long-term; and can have different foci, for example inventory control for a single product, for a single store, or across an entire supply chain. Organisations that face such challenges include businesses, not-for-profit organisations, and policymakers who address societal challenges.
Forecasts of quantities that adhere to some known constraints should be coherent; that is, the predicted values at disaggregate scales should add up to the aggregate forecast. For example, monthly predictions should sum up to annual predictions and similarly, regional predictions should add up to country-level predictions. This is an important qualifier for forecasts to support aligned decision making across different planning units and horizons.
This forecasting problem setting gives rise to hierarchical forecasting. Historically this has been addressed using Top-Down and Bottom-Up approaches, which have been shown to exhibit several limitations. In the past decade, the introduction of forecast reconciliation approaches has reinvigorated research into hierarchical forecasting. Recent work has looked at novel estimation techniques (Eckert et al, 2020), expansion of the hierarchical framework to temporal and cross-temporal hierarchies (Kourentzes and Athanasopoulos, 2019), probabilistic hierarchical forecasting (Ben Taieb et al, 2020, Jeon et al., 2019), alternative understandings of the problem through a geometric or optimisation lens (Panagiotelis et al, 2020; Nystrup et al, 2019; van Erven and Cugliari, 2015), amongst many other contributions. Meanwhile, forecast reconciliation techniques have been applied to a number of novel domains including energy (Yagli et al, 2020), macroeconomics (Athanasopoulos et al, 2020), mortality (Li and Tang, 2019), tourism (Kourentzes and Athanasopoulos, 2019), and intermittent demand (Kourentzes and Athanasopoulos, 2021).
To support the discussion in hierarchical forecasting research we have organised a special issue at the International Journal of Forecasting.
The purpose of the special issue is to attract high quality papers that are concerned with hierarchical forecasting, from various related disciplines: Artificial Intelligence (AI), econometrics, machine learning, management, operational research, statistics, etc.
Areas of interest include, but are not limited to:
- New methodologies for hierarchical forecasting
- High dimensional hierarchical forecasting (methods and applications)
- An improved understanding of the relationship between forecast reconciliation and forecast combination
- Probabilistic hierarchical forecasting
- Temporal and cross-temporal hierarchies
- Machine learning and AI approaches to hierarchical forecasting
- Hierarchical forecasting with explanatory variables
- Applications of hierarchical forecasting to new domains
Submission Guidelines:
To submit a paper for consideration for the Special Issue, please upload your paper online and include a cover letter indicating that the paper is for the special issue “Innovations in Hierarchical Forecasting”.
Contacts: George Athanasopoulos, Rob J Hyndman, Anastasios Panagiotelis and Nikolaos Kourentzes
Important Dates:
Submission starts: 1 December 2020
Submission deadline: 31 August 2021
Athanasopoulos, G., Gamakumara, P., Panagiotelis, A., Hyndman, R. J., & Affan, M. (2020). Hierarchical forecasting. In Macroeconomic Forecasting in the Era of Big Data (pp. 689-719). Springer, Cham.
Ben Taieb, S., Taylor, J. W., & Hyndman, R. J. (2020). Hierarchical probabilistic forecasting of electricity demand with smart meter data. Journal of the American Statistical Association, 1-17.
Eckert, F., Hyndman, R. J., & Panagiotelis, A. (2020). Forecasting Swiss exports using Bayesian forecast reconciliation. European Journal of Operational Research, forthcoming.
Jeon, J., Panagiotelis, A., & Petropoulos, F. (2019). Probabilistic forecast reconciliation with applications to wind power and electric load. European Journal of Operational Research, 279(2), 364–379.
Kourentzes, N., & Athanasopoulos, G. (2019). Cross-temporal coherent forecasts for Australian tourism. Annals of Tourism Research, 75, 393-409.
Kourentzes, N., & Athanasopoulos, G. (2021). Elucidate structure in intermittent demand series. European Journal of Operational Research, 288 (1), 141-152
Li, H., & Tang, Q. (2019). Analyzing mortality bond indexes via hierarchical forecast reconciliation. ASTIN Bulletin: The Journal of the IAA, 49(3), 823-846.
Nystrup, P., Lindström, E., Pinson, P., & Madsen, H. (2020). Temporal hierarchies with autocorrelation for load forecasting. European Journal of Operational Research, 280(3), 876-888.
Panagiotelis, A., Athanasopoulos, G., Gamakumara, P., & Hyndman, R. J. (2020). Forecast reconciliation: A geometric view with new insights on bias correction. International Journal of Forecasting, forthcoming.
Van Erven, T., & Cugliari, J. (2015). Game-theoretically optimal reconciliation of contemporaneous hierarchical time series forecasts. In Modeling and stochastic learning for forecasting in high dimensions (pp. 297-317). Springer, Cham.
Yagli, G. M., Yang, D., & Srinivasan, D. (2020). Reconciling solar forecasts: Probabilistic forecasting with homoscedastic Gaussian errors on a geographical hierarchy. Solar Energy, 210(1), 59-67.