Joint Bottom-up Method for Probabilistic Forecasting of Hierarchical Time Series
Published Online:7 Jan 2025https://doi.org/10.1287/opre.2022.0113
References
- 1993) Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. 88(422):669–679.Crossref, Google Scholar (
- 2017) Forecasting with temporal hierarchies. Eur. J. Oper. Res. 262(1):60–74.Crossref, Google Scholar (
- 2020) Hierarchical forecasting. Fuleky P, ed. Macroeconomic Forecasting in the Era of Big Data (Springer, Berlin, Heidelberg), 689–719.Crossref, Google Scholar (
- 2000) Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Statist. Sinica 10:1281–1311.Google Scholar (
- 2017) Modelling the interdependence of tourism demand: The global vector autoregressive approach. Ann. Tourism Res. 67:1–13.Crossref, Google Scholar (
- 2023) Constructing quantiles via forecast errors: Theory and empirical evidence. Preprint, submitted February 27, https://dx.doi.org/10.2139/ssrn.4371538.Google Scholar (
- 2011) A multiproduct risk-averse newsvendor with law-invariant coherent measures of risk. Oper. Res. 59(2):346–364.Link, Google Scholar (
- 2021) Forecast combination based forecast reconciliation: Insights and extensions. Preprint, submitted June 10, https://arxiv.org/abs/2106.05653.Google Scholar (
- 1976) Aggregate versus subaggregate models in local area forecasting. J. Amer. Statist. Assoc. 71(353):68–71.Crossref, Google Scholar (
- 2021) Forecasting Swiss exports using Bayesian forecast reconciliation. Eur. J. Oper. Res. 291(2):693–710.Crossref, Google Scholar (
- 1996) RATS Handbook: Handbook for Econometric Time Series (Wiley, New York).Google Scholar (
- 2008) Sure independence screening for ultrahigh dimensional feature space. J. Roy. Statist. Soc. Ser. B (Statist. Methodological) 70(5):849.Crossref, Google Scholar (
- 2001) The Elements of Statistical Learning , vol. 1 (Springer, New York).Google Scholar (
- 2018) Probabilistic forecasts in hierarchical time series. Technical report, Department of Econometrics and Business Statistics, Monash University, VIC, Australia.Google Scholar (
- 2008) A weakly informative default prior distribution for logistic and other regression models. Ann. Appl. Statist. 2(4):1360–1383.Crossref, Google Scholar (
- 1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intelligence 6:721–741.Crossref, Google Scholar (
- 2014) On the distribution of sums of random variables with copula-induced dependence. Insurance: Math. Econom. 59:27–44.Crossref, Google Scholar (
- 2011) Making and evaluating point forecasts. J. Amer. Statist. Assoc. 106(494):746–762.Crossref, Google Scholar (
- 2014) Probabilistic forecasting. Annu. Rev. Statist. Appl. 1:125–151.Crossref, Google Scholar (
- 2007) Strictly proper scoring rules, prediction, and estimation. J. Amer. Statist. Assoc. 102(477):359–378.Crossref, Google Scholar (
- 1994) Time Series Analysis (Princeton University Press, Princeton, NJ).Crossref, Google Scholar (
- 2014) A note on the evaluation of novel biomarkers: Do not rely on integrated discrimination improvement and net reclassification index. Statist. Medicine 33(19):3405–3414.Crossref, Google Scholar (
- 2021) Understanding forecast reconciliation. Eur. J. Oper. Res. 294(1):149–160.Crossref, Google Scholar (
- 2008) Automatic time series forecasting: The forecast package for R. J. Statist. Software 26(3):1–22.Google Scholar (
- 2011) Optimal combination forecasts for hierarchical time series. Comput. Statist. Data Anal. (Oxford) 55(9):2579–2589.Crossref, Google Scholar (
- 2003) Detecting differentially expressed genes in microarrays using Bayesian model selection. J. Amer. Statist. Assoc. 98(462):438–455.Crossref, Google Scholar (
- 2005) Spike and slab variable selection: Frequentist and Bayesian strategies. Ann. Statist. 33(2):730–773.Crossref, Google Scholar (
- 2011) Consistency of spike and slab regression. Statist. Probability Lett. 81(12):1920–1928.Crossref, Google Scholar (
- 2014) Geometry and properties of generalized ridge regression in high dimensions. Contemporary Math. 622:81–93.Crossref, Google Scholar (
- 2019) Probabilistic forecast reconciliation with applications to wind power and electric load. Eur. J. Oper. Res. 279(2):364–379.Crossref, Google Scholar (
- 1997) Numerical methods for estimation and inference in Bayesian VAR-models. J. Appl. Econom. 12(2):99–132.Crossref, Google Scholar (
- 2019) Analyzing mortality bond indexes via hierarchical forecast reconciliation. ASTIN Bull. 49(3):823–846.Crossref, Google Scholar (
- 2018) The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31–57.Crossref, Google Scholar (
- 2007) Time Series Analysis (CRC Press, Boca Raton, FL).Crossref, Google Scholar (
- 2022) The m5 competition: Background, organization, and implementation. Internat. J. Forecasting 38(4):1325–1336.Crossref, Google Scholar (
- 2018) Comparing spike and slab priors for Bayesian variable selection. Preprint, submitted December 18, https://arxiv.org/abs/1812.07259.Google Scholar (
- 1976) Scoring rules for continuous probability distributions. Management Sci. 22(10):1087–1096.Link, Google Scholar (
- 1983) Generalized Linear Models (Routledge, London).Crossref, Google Scholar (
- 1988) Bayesian variable selection in linear regression. J. Amer. Statist. Assoc. 83(404):1023–1032.Crossref, Google Scholar (
- 2005) Bayesian estimates for vector autoregressive models. J. Bus. Econom. Statist. 23(1):105–117.Crossref, Google Scholar (
- 2017) A Bayesian model for forecasting hierarchically structured time series. Preprint, submitted November 13, https://arxiv.org/abs/1711.04738.Google Scholar (
- 2019) Assessing the performance of hierarchical forecasting methods on the retail sector. Entropy 21(4):436.Crossref, Google Scholar (
- 2016) Systematic risk in supply chain networks. Management Sci. 62(6):1755–1777.Link, Google Scholar (
- 2017) Integrated hierarchical forecasting. Eur. J. Oper. Res. 263(2):412–418.Crossref, Google Scholar (
- 2016) “Why should I trust you?” Explaining the predictions of any classifier. Proc. 22nd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1135–1144.Google Scholar (
- 2005) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statist. Appl. Genetics Molecular Biology 4(1):1–30.Google Scholar (
- 2000) Decision bias in the newsvendor problem with a known demand distribution: Experimental evidence. Management Sci. 46(3):404–420.Link, Google Scholar (
- 2006) Does monetary policy generate recessions? Macroeconom. Dynamics 10(2):231–272.Crossref, Google Scholar (
- 2021) Product sales probabilistic forecasting: An empirical evaluation using the m5 competition data. Internat. J. Production Econom. 240:108237.Crossref, Google Scholar (
- 1970) A family of strictly proper scoring rules which are sensitive to distance. J. Appl. Meteorology 9(3):360–364.Crossref, Google Scholar (
- Taieb SB, Taylor JW, Hyndman RJ (2017a) Coherent probabilistic forecasts for hierarchical time series. Precup D, Teh Y, eds. Proc. 34th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research (PMLR, New York), 3348–3357.Google Scholar
- 2021) Hierarchical probabilistic forecasting of electricity demand with smart meter data. J. Amer. Statist. Assoc. 116(533):27–43.Crossref, Google Scholar (
- Taieb SB, Yu J, Barreto M, Rajagopal R (2017b) Regularization in hierarchical time series forecasting with application to electricity smart meter data. Proc. AAAI Conf. Artificial Intelligence 31(1):4474–4480.Google Scholar
- Tourism Research Australia (2015) Tourism forecasts. Technical report, Australian Government, Canberra, Australia.Google Scholar
- 2021) Data Science for Supply Chain Forecasting (Walter de Gruyter GmbH & Co, Berlin).Crossref, Google Scholar (
- 2019) Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. J. Amer. Statist. Assoc. 114(526):804–819.Crossref, Google Scholar (
- 1981) Combining probability distributions from dependent information sources. Management Sci. 27(4):479–488.Link, Google Scholar (