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Prediction intervals for multiplicative Holt-Winters

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  • Chatfield, Chris
  • Yar, Mohammed
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    File URL: http://www.sciencedirect.com/science/article/B6V92-45PMMPV-5/2/3317f7c0b72492bfa1c351045829fb11
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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 7 (1991)
    Issue (Month): 1 (May)
    Pages: 31-37

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    Handle: RePEc:eee:intfor:v:7:y:1991:i:1:p:31-37

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    Web page: http://www.elsevier.com/locate/ijforecast

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    Cited by:
    1. Weller, Barry R., 1995. "Software review," International Journal of Forecasting, Elsevier, vol. 11(1), pages 175-187, March.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901, July.
    4. Cote, Murray J., 2005. "A note on "Bed allocation techniques based on census data"," Socio-Economic Planning Sciences, Elsevier, vol. 39(2), pages 183-192, June.
    5. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286.
    6. J. Bermúdez & J. Segura & E. Vercher, 2008. "SIOPRED: a prediction and optimisation integrated system for demand," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 16(2), pages 258-271, December.
    7. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/01, Monash University, Department of Econometrics and Business Statistics.
    8. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    9. Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
    10. Chen, Chunhang, 1997. "Robustness properties of some forecasting methods for seasonal time series: A Monte Carlo study," International Journal of Forecasting, Elsevier, vol. 13(2), pages 269-280, June.
    11. Bianchi, Lisa & Jarrett, Jeffrey & Choudary Hanumara, R., 1998. "Improving forecasting for telemarketing centers by ARIMA modeling with intervention," International Journal of Forecasting, Elsevier, vol. 14(4), pages 497-504, December.
    12. Victor Guerrero & Edmundo Berumen, 1998. "Forecasting electricity consumption with extra-model information provided by consumers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(2), pages 283-299.
    13. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    14. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.
    15. Lotze, Thomas H. & Shmueli, Galit, 2009. "How does improved forecasting benefit detection? An application to biosurveillance," International Journal of Forecasting, Elsevier, vol. 25(3), pages 467-483, July.
    16. Mick Silver, 2006. "Core Inflation Measures and Statistical Issues in Choosing Among them," IMF Working Papers 06/97, International Monetary Fund.
    17. Bermudez, J.D. & Segura, J.V. & Vercher, E., 2006. "A decision support system methodology for forecasting of time series based on soft computing," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 177-191, November.

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