References in zbMATH (referenced in 80 articles )

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  1. 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)
  2. Bajalinov, E.; Duleba, Sz.: Seasonal time series forecasting by the Walsh-transformation based technique (2020)
  3. Bildosola, Iñaki; Garechana, Gaizka; Zarrabeitia, Enara; Cilleruelo, Ernesto: Characterization of strategic emerging technologies: the case of big data (2020)
  4. Bozikas, Apostolos; Pitselis, Georgios: Incorporating crossed classification credibility into the Lee-Carter model for multi-population mortality data (2020)
  5. Li, Degui; Robinson, Peter M.; Shang, Han Lin: Long-range dependent curve time series (2020)
  6. Li, Yang; Zhu, Zhengyuan: Spatio-temporal modeling of global ozone data using convolution (2020)
  7. Lowther, Aaron P.; Fearnhead, Paul; Nunes, Matthew A.; Jensen, Kjeld: Semi-automated simultaneous predictor selection for regression-SARIMA models (2020)
  8. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)
  9. Shang, Han Lin: Dynamic principal component regression for forecasting functional time series in a group structure (2020)
  10. Shang, Han Lin; Haberman, Steven: Forecasting multiple functional time series in a group structure: an application to mortality (2020)
  11. Spiliotis, Evangelos; Assimakopoulos, Vassilios; Makridakis, Spyros: Generalizing the Theta method for automatic forecasting (2020)
  12. Wickramasuriya, Shanika L.; Turlach, Berwin A.; Hyndman, Rob J.: Optimal non-negative forecast reconciliation (2020)
  13. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  14. Di Gangi, Leonardo; Lapucci, M.; Schoen, F.; Sortino, A.: An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series (2019)
  15. Goin, Dana E.; Ahern, Jennifer: Identification of spikes in time series (2019)
  16. Huber, Jakob; Müller, Sebastian; Fleischmann, Moritz; Stuckenschmidt, Heiner: A data-driven newsvendor problem: from data to decision (2019)
  17. Khan, Atikur R.; Hassani, Hossein: Dependence measures for model selection in singular spectrum analysis (2019)
  18. Li, Han; Tang, Qihe: Analyzing mortality bond indexes via hierarchical forecast reconciliation (2019)
  19. Peña, Daniel; Smucler, Ezequiel; Yohai, Victor J.: Forecasting multiple time series with one-sided dynamic principal components (2019)
  20. Rendon-Sanchez, Juan F.; de Menezes, Lilian M.: Structural combination of seasonal exponential smoothing forecasts applied to load forecasting (2019)

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