Forecasting with exponential smoothing. The state space approach Exponential smoothing methods have been around since the 1950s, and are the most popular forecasting methods used in business and industry. Recently, exponential smoothing has been revolutionized with the introduction of a complete modeling framework incorporating innovations state space models, likelihood calculation, prediction intervals and procedures for model selection. In this book, all of the important results for this framework are brought together in a coherent manner with consistent notation. In addition, many new results and extensions are introduced and several application areas are examined in detail.

References in zbMATH (referenced in 21 articles )

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  1. Barrow, Devon; Kourentzes, Nikolaos: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days (2018)
  2. Pennings, Clint L.P.; van Dalen, Jan: Integrated hierarchical forecasting (2017)
  3. Elatraby, Amr I.A.; Saad, Hisham Mohamed Abdelaziz: Forecasting the exchange rate of the Egyptian pound against the U.S. Dollar: an empirical study (2016)
  4. Kosiorowski, Daniel: Dilemmas of robust analysis of economic data streams (2016)
  5. Rubio, Abel; Bermúdez, José D.; Vercher, Enriqueta: Forecasting portfolio returns using weighted fuzzy time series methods (2016)
  6. Bernardi, Mauro; Petrella, Lea: Multiple seasonal cycles forecasting model: the Italian electricity demand (2015)
  7. Casarin, Roberto: Comment on article by Windle and Carvalho (2014)
  8. Chapados, Nicolas; Joliveau, Marc; L’Ecuyer, Pierre; Rousseau, Louis-Martin: Retail store scheduling for profit (2014)
  9. Coelho, Leandro C.; Cordeau, Jean-François; Laporte, Gilbert: Heuristics for dynamic and stochastic inventory-routing (2014)
  10. Pesaran, M.Hashem; Pick, Andreas; Pranovich, Mikhail: Optimal forecasts in the presence of structural breaks (2013)
  11. Yager, Ronald R.: Exponential smoothing with credibility weighted observations (2013)
  12. Vercher, E.; Corberán-Vallet, A.; Segura, J.V.; Bermúdez, J.D.: Initial conditions estimation for improving forecast accuracy in exponential smoothing (2012)
  13. De Livera, Alysha M.; Hyndman, Rob J.; Snyder, Ralph D.: Forecasting time series with complex seasonal patterns using exponential smoothing (2011)
  14. Shang, Han Lin; Hyndman, Rob.J.: Nonparametric time series forecasting with dynamic updating (2011)
  15. Lau, Ada; McSharry, Patrick: Approaches for multi-step density forecasts with application to aggregated wind power (2010)
  16. Neves, Maria Manuela; Cordeiro, Clara: Exponential smoothing and resampling techniques in time series prediction (2010)
  17. Taylor, James W.: Triple seasonal methods for short-term electricity demand forecasting (2010)
  18. Akram, Muhammad; Hyndman, Rob J.; Ord, J.Keith: Exponential smoothing and non-negative data (2009)
  19. Hyndman, Rob J.; Shang, Han Lin: Forecasting functional time series (2009)
  20. Fildes, R.; Nikolopoulos, K.; Crone, S.F.; Syntetos, A.A.: Forecasting and operational research: a review (2008)

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