R package expsmooth: Data Sets from ”Forecasting with Exponential Smoothing”. Data sets from the book ”Forecasting with exponential smoothing: the state space approach” by Hyndman, Koehler, Ord and Snyder (Springer, 2008). 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 39 articles )

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  1. Kourentzes, Nikolaos; Athanasopoulos, George: Elucidate structure in intermittent demand series (2021)
  2. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  3. Wick, Felix; Kerzel, Ulrich; Hahn, Martin; Wolf, Moritz; Singhal, Trapti; Stemmer, Daniel; Ernst, Jakob; Feindt, Michael: Demand forecasting of individual probability density functions with machine learning (2021)
  4. Nystrup, Peter; Lindström, Erik; Pinson, Pierre; Madsen, Henrik: Temporal hierarchies with autocorrelation for load forecasting (2020)
  5. Huber, Jakob; Müller, Sebastian; Fleischmann, Moritz; Stuckenschmidt, Heiner: A data-driven newsvendor problem: from data to decision (2019)
  6. Rendon-Sanchez, Juan F.; de Menezes, Lilian M.: Structural combination of seasonal exponential smoothing forecasts applied to load forecasting (2019)
  7. Barrow, Devon; Kourentzes, Nikolaos: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days (2018)
  8. Bayer, Fábio M.; Cintra, Renato J.; Cribari-Neto, Francisco: Beta seasonal autoregressive moving average models (2018)
  9. Bergmeir, Christoph; Hyndman, Rob J.; Koo, Bonsoo: A note on the validity of cross-validation for evaluating autoregressive time series prediction (2018)
  10. Taylor, James W.; Jeon, Jooyoung: Probabilistic forecasting of wave height for offshore wind turbine maintenance (2018)
  11. Yapar, Guckan; Capar, Sedat; Selamlar, Hanife Taylan; Yavuz, İdil: Modified Holt’s linear trend method (2018)
  12. Athanasopoulos, George; Hyndman, Rob J.; Kourentzes, Nikolaos; Petropoulos, Fotios: Forecasting with temporal hierarchies (2017)
  13. Nikolakopoulos, Athanassios; Ganas, Ioannis: Economic model predictive inventory routing and control (2017)
  14. Pennings, Clint L. P.; van Dalen, Jan: Integrated hierarchical forecasting (2017)
  15. Tyralis, Hristos; Papacharalampous, Georgia: Variable selection in time series forecasting using random forests (2017)
  16. Hyndman, Rob J.; Lee, Alan J.; Wang, Earo: Fast computation of reconciled forecasts for hierarchical and grouped time series (2016)
  17. Kosiorowski, Daniel: Dilemmas of robust analysis of economic data streams (2016)
  18. Pedraza, Luis F.; Hernandez, Cesar A.; Paez, Ingrid P.; Ortiz, Jorge E.; Rodriguez-Colina, E.: Linear algorithms for radioelectric spectrum forecast (2016)
  19. Rubio, Abel; Bermúdez, José D.; Vercher, Enriqueta: Forecasting portfolio returns using weighted fuzzy time series methods (2016)
  20. Bernardi, Mauro; Petrella, Lea: Multiple seasonal cycles forecasting model: the Italian electricity demand (2015)

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