Forecast

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.

Keywords for this software

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References in zbMATH (referenced in 71 articles )

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  1. Bildosola, Iñaki; Garechana, Gaizka; Zarrabeitia, Enara; Cilleruelo, Ernesto: Characterization of strategic emerging technologies: the case of big data (2020)
  2. Bozikas, Apostolos; Pitselis, Georgios: Incorporating crossed classification credibility into the Lee-Carter model for multi-population mortality data (2020)
  3. Li, Yang; Zhu, Zhengyuan: Spatio-temporal modeling of global ozone data using convolution (2020)
  4. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)
  5. Shang, Han Lin: Dynamic principal component regression for forecasting functional time series in a group structure (2020)
  6. Shang, Han Lin; Haberman, Steven: Forecasting multiple functional time series in a group structure: an application to mortality (2020)
  7. Spiliotis, Evangelos; Assimakopoulos, Vassilios; Makridakis, Spyros: Generalizing the Theta method for automatic forecasting (2020)
  8. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  9. 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)
  10. Goin, Dana E.; Ahern, Jennifer: Identification of spikes in time series (2019)
  11. Huber, Jakob; Müller, Sebastian; Fleischmann, Moritz; Stuckenschmidt, Heiner: A data-driven newsvendor problem: from data to decision (2019)
  12. Khan, Atikur R.; Hassani, Hossein: Dependence measures for model selection in singular spectrum analysis (2019)
  13. Li, Han; Tang, Qihe: Analyzing mortality bond indexes via hierarchical forecast reconciliation (2019)
  14. Peña, Daniel; Smucler, Ezequiel; Yohai, Victor J.: Forecasting multiple time series with one-sided dynamic principal components (2019)
  15. Rendon-Sanchez, Juan F.; de Menezes, Lilian M.: Structural combination of seasonal exponential smoothing forecasts applied to load forecasting (2019)
  16. Santos, James D.; Costa, José M. J.: An algorithm for prior elicitation in dynamic Bayesian models for proportions with the logit link function (2019)
  17. Shang, Han Lin: Dynamic principal component regression: application to age-specific mortality forecasting (2019)
  18. Shang, Han Lin; Yang, Yang; Kearney, Fearghal: Intraday forecasts of a volatility index: functional time series methods with dynamic updating (2019)
  19. Uddin, Gazi Salah; Gençay, Ramazan; Bekiros, Stelios; Sahamkhadam, Maziar: Enhancing the predictability of crude oil markets with hybrid wavelet approaches (2019)
  20. Wickramasuriya, Shanika L.; Athanasopoulos, George; Hyndman, Rob J.: Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization (2019)

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