References in zbMATH (referenced in 87 articles )

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  1. Eckert, Florian; Hyndman, Rob J.; Panagiotelis, Anastasios: Forecasting Swiss exports using Bayesian forecast reconciliation (2021)
  2. Kourentzes, Nikolaos; Athanasopoulos, George: Elucidate structure in intermittent demand series (2021)
  3. Ma, Shaohui; Fildes, Robert: Retail sales forecasting with meta-learning (2021)
  4. Taieb, Souhaib Ben; Taylor, James W.; Hyndman, Rob J.: Hierarchical probabilistic forecasting of electricity demand with smart meter data (2021)
  5. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  6. Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Interval forecasts based on regression trees for streaming data (2021)
  7. Alexandrov, Alexander; Benidis, Konstantinos; Bohlke-Schneider, Michael; Flunkert, Valentin; Gasthaus, Jan; Januschowski, Tim; Maddix, Danielle C.; Rangapuram, Syama; Salinas, David; Schulz, Jasper; Stella, Lorenzo; Türkmen, Ali Caner; Wang, Yuyang: GluonTS: probabilistic and neural time series modeling in Python (2020)
  8. Atance, David; Balbás, Alejandro; Navarro, Eliseo: Constructing dynamic life tables with a single-factor model (2020)
  9. Bajalinov, E.; Duleba, Sz.: Seasonal time series forecasting by the Walsh-transformation based technique (2020)
  10. Bildosola, Iñaki; Garechana, Gaizka; Zarrabeitia, Enara; Cilleruelo, Ernesto: Characterization of strategic emerging technologies: the case of big data (2020)
  11. Bozikas, Apostolos; Pitselis, Georgios: Incorporating crossed classification credibility into the Lee-Carter model for multi-population mortality data (2020)
  12. Li, Degui; Robinson, Peter M.; Shang, Han Lin: Long-range dependent curve time series (2020)
  13. Li, Yang; Zhu, Zhengyuan: Spatio-temporal modeling of global ozone data using convolution (2020)
  14. Lowther, Aaron P.; Fearnhead, Paul; Nunes, Matthew A.; Jensen, Kjeld: Semi-automated simultaneous predictor selection for regression-SARIMA models (2020)
  15. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)
  16. Shang, Han Lin: Dynamic principal component regression for forecasting functional time series in a group structure (2020)
  17. Shang, Han Lin; Haberman, Steven: Forecasting multiple functional time series in a group structure: an application to mortality (2020)
  18. Smirnov, Dmitry; Huchzermeier, Arnd: Analytics for labor planning in systems with load-dependent service times (2020)
  19. Spiliotis, Evangelos; Assimakopoulos, Vassilios; Makridakis, Spyros: Generalizing the Theta method for automatic forecasting (2020)
  20. Wickramasuriya, Shanika L.; Turlach, Berwin A.; Hyndman, Rob J.: Optimal non-negative forecast reconciliation (2020)

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