forecast

R package forecast: Forecasting functions for time series and linear models , Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. (Source: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 97 articles , 1 standard article )

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  1. Basellini, Ugofilippo; Kjærgaard, Søren; Camarda, Carlo Giovanni: An age-at-death distribution approach to forecast cohort mortality (2020)
  2. Bildosola, Iñaki; Garechana, Gaizka; Zarrabeitia, Enara; Cilleruelo, Ernesto: Characterization of strategic emerging technologies: the case of big data (2020)
  3. Bozikas, Apostolos; Pitselis, Georgios: Incorporating crossed classification credibility into the Lee-Carter model for multi-population mortality data (2020)
  4. Esam Mahdi: portes: An R Package for Portmanteau Tests in Time Series Models (2020) arXiv
  5. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  6. Li, Yang; Zhu, Zhengyuan: Spatio-temporal modeling of global ozone data using convolution (2020)
  7. Neeraj Dhanraj Bokde; Gorm Bruun Andersen: ForecastTB - An R Package as a Test-bench for Forecasting Methods Comparison (2020) arXiv
  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. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  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. Guibert, Quentin; Lopez, Olivier; Piette, Pierrick: Forecasting mortality rate improvements with a high-dimensional VAR (2019)
  17. Huber, Jakob; Müller, Sebastian; Fleischmann, Moritz; Stuckenschmidt, Heiner: A data-driven newsvendor problem: from data to decision (2019)
  18. Khan, Atikur R.; Hassani, Hossein: Dependence measures for model selection in singular spectrum analysis (2019)
  19. Li, Han; Tang, Qihe: Analyzing mortality bond indexes via hierarchical forecast reconciliation (2019)
  20. Peña, Daniel; Smucler, Ezequiel; Yohai, Victor J.: Forecasting multiple time series with one-sided dynamic principal components (2019)

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