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 123 articles , 1 standard article )

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  1. Adithi R. Upadhya, Pratyush Agrawal, Sreekanth Vakacherla, Meenakshi Kushwaha: pollucheck v1.0: A package to explore open-source air pollution data (2021) not zbMATH
  2. David Salinas, Valentin Flunkert, Jan Gasthaus: DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2021) arXiv
  3. Eckert, Florian; Hyndman, Rob J.; Panagiotelis, Anastasios: Forecasting Swiss exports using Bayesian forecast reconciliation (2021)
  4. Kourentzes, Nikolaos; Athanasopoulos, George: Elucidate structure in intermittent demand series (2021)
  5. Lange, Henning; Brunton, Steven L.; Kutz, J. Nathan: From Fourier to Koopman: spectral methods for long-term time series prediction (2021)
  6. Ma, Shaohui; Fildes, Robert: Retail sales forecasting with meta-learning (2021)
  7. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  8. Taieb, Souhaib Ben; Taylor, James W.; Hyndman, Rob J.: Hierarchical probabilistic forecasting of electricity demand with smart meter data (2021)
  9. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  10. Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Interval forecasts based on regression trees for streaming data (2021)
  11. 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)
  12. Atance, David; Balbás, Alejandro; Navarro, Eliseo: Constructing dynamic life tables with a single-factor model (2020)
  13. Bajalinov, E.; Duleba, Sz.: Seasonal time series forecasting by the Walsh-transformation based technique (2020)
  14. Basellini, Ugofilippo; Kjærgaard, Søren; Camarda, Carlo Giovanni: An age-at-death distribution approach to forecast cohort mortality (2020)
  15. Bildosola, Iñaki; Garechana, Gaizka; Zarrabeitia, Enara; Cilleruelo, Ernesto: Characterization of strategic emerging technologies: the case of big data (2020)
  16. Bozikas, Apostolos; Pitselis, Georgios: Incorporating crossed classification credibility into the Lee-Carter model for multi-population mortality data (2020)
  17. Esam Mahdi: portes: An R Package for Portmanteau Tests in Time Series Models (2020) arXiv
  18. Izhar Asael Alonzo Matamoros, Alicia Nieto-Reyes: An R package for Normality in Stationary Processes (2020) arXiv
  19. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  20. Li, Degui; Robinson, Peter M.; Shang, Han Lin: Long-range dependent curve time series (2020)

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