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:

References in zbMATH (referenced in 81 articles , 1 standard article )

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  1. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)
  2. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  3. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  4. Guibert, Quentin; Lopez, Olivier; Piette, Pierrick: Forecasting mortality rate improvements with a high-dimensional VAR (2019)
  5. Huber, Jakob; Müller, Sebastian; Fleischmann, Moritz; Stuckenschmidt, Heiner: A data-driven newsvendor problem: from data to decision (2019)
  6. Jeon, Jooyoung; Panagiotelis, Anastasios; Petropoulos, Fotios: Probabilistic forecast reconciliation with applications to wind power and electric load (2019)
  7. Khan, Atikur R.; Hassani, Hossein: Dependence measures for model selection in singular spectrum analysis (2019)
  8. Li, Han; Tang, Qihe: Analyzing mortality bond indexes via hierarchical forecast reconciliation (2019)
  9. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  10. Rendon-Sanchez, Juan F.; de Menezes, Lilian M.: Structural combination of seasonal exponential smoothing forecasts applied to load forecasting (2019)
  11. Santos, James D.; Costa, José M. J.: An algorithm for prior elicitation in dynamic Bayesian models for proportions with the logit link function (2019)
  12. Shang, Han Lin: Dynamic principal component regression: application to age-specific mortality forecasting (2019)
  13. Sophie Achard and Irène Gannaz: Wavelet-Based and Fourier-Based Multivariate Whittle Estimation: multiwave (2019) not zbMATH
  14. Uddin, Gazi Salah; Gençay, Ramazan; Bekiros, Stelios; Sahamkhadam, Maziar: Enhancing the predictability of crude oil markets with hybrid wavelet approaches (2019)
  15. Wickramasuriya, Shanika L.; Athanasopoulos, George; Hyndman, Rob J.: Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization (2019)
  16. Yeo, Kyongmin; Melnyk, Igor: Deep learning algorithm for data-driven simulation of noisy dynamical system (2019)
  17. Al-Douri, Yamur K.; Hamodi, Hussan; Lundberg, Jan: Time series forecasting using a two-level multi-objective genetic algorithm: a case study of maintenance cost data for tunnel fans (2018)
  18. Andrés Villegas; Vladimir Kaishev; Pietro Millossovich: StMoMo: An R Package for Stochastic Mortality Modeling (2018) not zbMATH
  19. Barrow, Devon; Kourentzes, Nikolaos: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days (2018)
  20. Chu, Ba; Huynh, Kim; Jacho-Chávez, David; Kryvtsov, Oleksiy: On the evolution of the united kingdom price distributions (2018)

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