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Exponential Smoothing Model Selection for Forecasting

Citations

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Cited by:

  1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
  2. Ralph D Snyder, 2005. "A Pedant's Approach to Exponential Smoothing," Monash Econometrics and Business Statistics Working Papers 5/05, Monash University, Department of Econometrics and Business Statistics.
  3. Chin-Yin Huang & Philip K.P. Lin, 2014. "Application of integrated data mining techniques in stock market forecasting," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-18, December.
  4. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
  5. Mun, Mak Kit & Chong, Choo Wei, 2018. "Forecasting Movie Demand Using Total and Split Exponential Smoothing," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(2), pages 81-94.
  6. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics.
  7. Hema, M. & Kumar, Ranjit & Singh, N.P., 2007. "Volatile Price and Declining Profitability of Black Pepper in India: Disquieting Future," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 20(1).
  8. Kumar, Ranjit & Singh, N.P. & Singh, R.P. & Vasisht, A.K., 2006. "Rural Infrastructure and Agricultural Growth: Interdependence and Variability in Indo-Gangetic Plains of India," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 61(3), pages 1-12.
  9. Ralph Snyder & Adrian Beaumont & J. Keith Ord, 2012. "Intermittent demand forecasting for inventory control: A multi-series approach," Monash Econometrics and Business Statistics Working Papers 15/12, Monash University, Department of Econometrics and Business Statistics.
  10. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.
  11. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660, July.
  12. Francisco Zamora-Martínez & Pablo Romeu & Paloma Botella-Rocamora & Juan Pardo, 2013. "Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis," Energies, MDPI, vol. 6(9), pages 1-21, September.
  13. Bartosz Bieganowski & Robert Slepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers 2404.01866, arXiv.org.
  14. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
  15. Izabela Dembińska & Agnieszka Barczak & Katarzyna Szopik-Depczyńska & Irena Dul & Adam Koliński & Giuseppe Ioppolo, 2022. "The Impact of the COVID-19 Pandemic on the Volume of Fuel Supplies to EU Countries," Energies, MDPI, vol. 15(22), pages 1-18, November.
  16. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
  17. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
  18. Qi, Lingzhi & Li, Xixi & Wang, Qiang & Jia, Suling, 2023. "fETSmcs: Feature-based ETS model component selection," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1303-1317.
  19. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
  20. Hisham Alghamdi & Ghulam Hafeez & Sajjad Ali & Safeer Ullah & Muhammad Iftikhar Khan & Sadia Murawwat & Lyu-Guang Hua, 2023. "An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid," Mathematics, MDPI, vol. 11(21), pages 1-22, November.
  21. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
  22. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
  23. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
  24. Ashton de Silva & Rob J. Hyndman & Ralph D. Snyder, 2007. "The vector innovation structural time series framework: a simple approach to multivariate forecasting," Monash Econometrics and Business Statistics Working Papers 3/07, Monash University, Department of Econometrics and Business Statistics.
  25. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
  26. Irena Lacka & Blazej Supron, 2021. "The Impact of COVID-19 on Road Freight Transport Evidence from Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 319-333.
  27. Longsheng Cheng & Mahboubeh Shadabfar & Arash Sioofy Khoojine, 2023. "A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets," Mathematics, MDPI, vol. 11(5), pages 1-34, February.
  28. Vladimir Simankov & Pavel Buchatskiy & Anatoliy Kazak & Semen Teploukhov & Stefan Onishchenko & Kirill Kuzmin & Petr Chetyrbok, 2024. "A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies," Energies, MDPI, vol. 17(2), pages 1-23, January.
  29. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
  30. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
  31. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
  32. Quang Hoc Tran & Yao-Min Fang & Tien-Yin Chou & Thanh-Van Hoang & Chun-Tse Wang & Van Truong Vu & Thi Lan Huong Ho & Quang Le & Mei-Hsin Chen, 2022. "Short-Term Traffic Speed Forecasting Model for a Parallel Multi-Lane Arterial Road Using GPS-Monitored Data Based on Deep Learning Approach," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
  33. Svetunkov, Ivan & Kourentzes, Nikolaos, 2015. "Complex Exponential Smoothing," MPRA Paper 69394, University Library of Munich, Germany.
  34. J W Taylor, 2011. "Multi-item sales forecasting with total and split exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 555-563, March.
  35. Syed Mithun Ali & Amanat Ur Rahman & Golam Kabir & Sanjoy Kumar Paul, 2024. "Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability," Sustainability, MDPI, vol. 16(6), pages 1-31, March.
  36. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.
  37. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
  38. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
  39. Bernard Moeketsi Hlalele, 2022. "A Comparative Analysis Of Holts-Winters’ And Neural Network Prediction Models On Annual Bloemfontein’S Precipitation: Risk Aversion," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 4(1), pages 17-21, April.
  40. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
  41. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
  42. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251.
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