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SIOPRED: a prediction and optimisation integrated system for demand

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Abstract

This paper presents a forecasting support system based on the generalised Holt-Winters exponential smoothing scheme to forecast time series of levels of demand. It is conceived as an integrated tool which has been implemented in Visual Basic. For improving the accuracy of automatic forecasting it uses an optimisation-based scheme which unifies the stages of estimation of the parameters and model selection. Based on this scheme, suitable forecasts and prediction intervals are obtained. The performance of the proposed system is compared with a number of well-established automatic forecasting procedures with respect to the 3003 time series included in the M3-competition.

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References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

    Article  Google Scholar 

  • Bermúdez JD, Segura JV, Vercher E (2006a) Improving demand forecasting accuracy using non-linear programming software. J Oper Res Soc 57:94–100

    Article  Google Scholar 

  • Bermúdez JD, Segura JV, Vercher E (2006b) A decision support system methodology for forecasting of time series based on soft computing. Comput Stat Data Anal 51:177–191

    Article  Google Scholar 

  • Bermúdez JD, Segura JV, Vercher E (2007a) Predicción del consumo de agua con SIOPRED. In: Rojas I, Pomares H (eds) Actas de simposio de inteligencia computacional SICO’2007. Thomson, Madrid, pp 447–451

    Google Scholar 

  • Bermúdez JD, Segura JV, Vercher E (2007b) Holt-Winters forecasting: an alternative formulation applied to UK air passenger data. J Appl Stat 34:1075–1090

    Article  Google Scholar 

  • Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis, forecasting and control, 3rd edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Chatfield C, Yar M (1991) Prediction Intervals for multiplicative Holt-Winters. Int J Forecast 7:31–37

    Article  Google Scholar 

  • Gardner ES Jr (1988) A simple method of computing prediction intervals for time series forecasts. Manag Sci 34:541–546

    Article  Google Scholar 

  • Gardner ES Jr (2006) Exponential smoothing: the state of the art—part II. Int J Forecast 22:637–666

    Article  Google Scholar 

  • Hyndman RJ, Koehler AB, Snyder RD, Grose S (2002) A state space framework automatic forecasting using exponential smoothing. Int J Forecast 18:439–454

    Article  Google Scholar 

  • Makridakis S, Hibon M (2000) The M3-competition: results, conclusions and implications. Int J Forecast 16:451–476

    Article  Google Scholar 

  • Ord JK, Koehler AB, Snyder RD (1997) Estimation and prediction for a class of dynamic nonlinear statistical models. J Am Stat Assoc 92:1621–1629

    Article  Google Scholar 

  • Segura JV, Vercher E (2001) A spreadsheet modelling approach to the Holt-Winters optimal forecasting. Eur J Oper Res 131:375–388

    Article  Google Scholar 

  • Segura JV, Vercher E (2002) A mathematical programming model for the Holt-Winters multiplicative method. Working paper I-2002-02, Centro de Investigación Operativa

  • Tashman LJ, Hoover J (2001) Diffusion of forecasting principles through software. In: Armstrong JS (ed) Principles of forecasting. Kluwer Academic, Boston, pp 651–676

    Google Scholar 

  • Theil H (1966) Applied economic forecasting. North-Holland, Amsterdam

    Google Scholar 

  • Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci 6:324–342

    Google Scholar 

  • Yu X, Kacprzyk J (eds) (2003) Applied decision support with soft computing, vol 124. Springer, Berlin

    Google Scholar 

  • Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Syst 1:45–55

    Article  Google Scholar 

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Correspondence to E. Vercher.

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Bermúdez, J.D., Segura, J.V. & Vercher, E. SIOPRED: a prediction and optimisation integrated system for demand. TOP 16, 258–271 (2008). https://doi.org/10.1007/s11750-008-0042-7

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  • DOI: https://doi.org/10.1007/s11750-008-0042-7

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