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 150 articles , 1 standard article )

Showing results 21 to 40 of 150.
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  1. Matthew Trupiano: The R Package knnwtsim: Nonparametric Forecasting With a Tailored Similarity Measure (2021) arXiv
  2. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  3. Ollech, Daniel: Seasonal adjustment of daily time series (2021)
  4. Peder Bacher, Hjörleifur G. Bergsteinsson, Linde Frölke, Mikkel L. Sørensen, Julian Lemos-Vinasco, Jon Liisberg, Jan Kloppenborg Møller, Henrik Aalborg Nielsen, Henrik Madsen: onlineforecast: An R package for adaptive and recursive forecasting (2021) arXiv
  5. Puindi, António Casimiro; Silva, Maria Eduarda: Dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns (2021)
  6. Sanna Passino, Francesco; Bertiger, Anna S.; Neil, Joshua C.; Heard, Nicholas A.: Link prediction in dynamic networks using random dot product graphs (2021)
  7. Taieb, Souhaib Ben; Taylor, James W.; Hyndman, Rob J.: Hierarchical probabilistic forecasting of electricity demand with smart meter data (2021)
  8. Tak, Nihat: Meta fuzzy functions based feed-forward neural networks with a single hidden layer for forecasting (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. Chakraborty, Tanujit; Ghosh, Indrajit: Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: a data-driven analysis (2020)
  18. Dal Molin Ribeiro, Matheus Henrique; da Silva, Ramon Gomes; Mariani, Viviana Cocco; dos Santos Coelho, Leandro: Short-term forecasting COVID-19 cumulative confirmed cases: perspectives for Brazil (2020)
  19. Esam Mahdi: portes: An R Package for Portmanteau Tests in Time Series Models (2020) arXiv
  20. Firmino, Paulo Renato Alves; de Sales, Jair Paulino; Gonçalves Júnior, Jucier; da Silva, Taciana Araújo: A non-central beta model to forecast and evaluate pandemics time series (2020)

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