fpp2

R package fpp2: Data for ”Forecasting: Principles and Practice” (2nd Edition). All data sets required for the examples and exercises in the book ”Forecasting: principles and practice” (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.


References in zbMATH (referenced in 31 articles )

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  1. Ashouri, Mahsa; Hyndman, Rob J.; Shmueli, Galit: Fast forecast reconciliation using linear models (2022)
  2. Bergeron-Boucher, Marie-Pier; Kjærgaard, Søren: Mortality forecasting at age 65 and above: an age-specific evaluation of the Lee-Carter model (2022)
  3. Goltsos, Thanos E.; Syntetos, Aris A.; Glock, Christoph H.; Ioannou, George: Inventory -- forecasting: mind the gap (2022)
  4. Hunter, Michael D.; Fatimah, Haya; Bornovalova, Marina A.: Two filtering methods of forecasting linear and nonlinear dynamics of intensive longitudinal data (2022)
  5. Nestler, Steffen; Humberg, Sarah: A Lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation (2022)
  6. Pizarroso, J., Portela, J., Muñoz, A: NeuralSens: Sensitivity Analysis of Neural Networks (2022) not zbMATH
  7. Saeed, Waddah: Frequency-based ensemble forecasting model for time series forecasting (2022)
  8. Urrutia, Patrick; Wren, David; Vogiatzis, Chrysafis; Yoshida, Ruriko: SARS-CoV-2 dissemination using a network of the US counties (2022)
  9. Arnerić, Josip: Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume (2021)
  10. Cetin, Beyza; Yavuz, Idil: Comparison of forecast accuracy of \textbfAtaand exponential smoothing (2021)
  11. Chatigny, Philippe; Patenaude, Jean-Marc; Wang, Shengrui: Spatiotemporal adaptive neural network for long-term forecasting of financial time series (2021)
  12. Gao, Guangyuan; Shi, Yanlin: Age-coherent extensions of the Lee-Carter model (2021)
  13. Hančová, Martina; Gajdoš, Andrej; Hanč, Jozef; Vozáriková, Gabriela: Estimating variances in time series kriging using convex optimization and empirical BLUPs (2021)
  14. Hollyman, Ross; Petropoulos, Fotios; Tipping, Michael E.: Understanding forecast reconciliation (2021)
  15. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  16. Kourentzes, Nikolaos; Athanasopoulos, George: Elucidate structure in intermittent demand series (2021)
  17. Li, Han; Hyndman, Rob J.: Assessing mortality inequality in the U.S.: what can be said about the future? (2021)
  18. Li, Hong; Shi, Yanlin: Forecasting mortality with international linkages: a global vector-autoregression approach (2021)
  19. Oikarinen, Emilia; Tiittanen, Henri; Henelius, Andreas; Puolamäki, Kai: Detecting virtual concept drift of regressors without ground truth values (2021)
  20. Smeekes, Stephan; Wijler, Etienne: An automated approach towards sparse single-equation cointegration modelling (2021)

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