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)

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 151 articles , 1 standard article )

Showing results 41 to 60 of 151.
Sorted by year (citations)
  1. 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)
  2. Izhar Asael Alonzo Matamoros, Alicia Nieto-Reyes: An R package for Normality in Stationary Processes (2020) arXiv
  3. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  4. Li, Degui; Robinson, Peter M.; Shang, Han Lin: Long-range dependent curve time series (2020)
  5. Li, Yang; Zhu, Zhengyuan: Spatio-temporal modeling of global ozone data using convolution (2020)
  6. Lowther, Aaron P.; Fearnhead, Paul; Nunes, Matthew A.; Jensen, Kjeld: Semi-automated simultaneous predictor selection for regression-SARIMA models (2020)
  7. Marina Knight, Kathryn Leeming, Guy Nason, Matthew Nunes: Generalized Network Autoregressive Processes and the GNAR Package (2020) not zbMATH
  8. Neeraj Dhanraj Bokde; Gorm Bruun Andersen: ForecastTB - An R Package as a Test-bench for Forecasting Methods Comparison (2020) arXiv
  9. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)
  10. Shang, Han Lin: Dynamic principal component regression for forecasting functional time series in a group structure (2020)
  11. Shang, Han Lin; Haberman, Steven: Forecasting multiple functional time series in a group structure: an application to mortality (2020)
  12. Silva, Isabel; Alonso, Hugo: New developments in the forecasting of monthly overnight stays in the north region of Portugal (2020)
  13. Smirnov, Dmitry; Huchzermeier, Arnd: Analytics for labor planning in systems with load-dependent service times (2020)
  14. Spiliotis, Evangelos; Assimakopoulos, Vassilios; Makridakis, Spyros: Generalizing the Theta method for automatic forecasting (2020)
  15. Wang, Earo; Cook, Dianne; Hyndman, Rob J.: A new tidy data structure to support exploration and modeling of temporal data (2020)
  16. Wickramasuriya, Shanika L.; Turlach, Berwin A.; Hyndman, Rob J.: Optimal non-negative forecast reconciliation (2020)
  17. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  18. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  19. 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)
  20. Goin, Dana E.; Ahern, Jennifer: Identification of spikes in time series (2019)