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Multi-period forecasting and scenario generation with limited data

Author

Listed:
  • Ignacio Rios
  • Roger Wets
  • David Woodruff

Abstract

Data for optimization problems often comes from (deterministic) forecasts, but it is naïve to consider a forecast as the only future possibility. A more sophisticated approach uses data to generate alternative future scenarios, each with an attached probability. The basic idea is to estimate the distribution of forecast errors and use that to construct the scenarios. Although sampling from the distribution of errors comes immediately to mind, we propose instead to approximate rather than sample. Benchmark studies show that the method we propose works well. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Ignacio Rios & Roger Wets & David Woodruff, 2015. "Multi-period forecasting and scenario generation with limited data," Computational Management Science, Springer, vol. 12(2), pages 267-295, April.
    • Handle: RePEc:spr:comgts:v:12:y:2015:i:2:p:267-295
    DOI: 10.1007/s10287-015-0230-5
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    References listed on IDEAS

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    1. Jitka Dupačová & Giorgio Consigli & Stein Wallace, 2000. "Scenarios for Multistage Stochastic Programs," Annals of Operations Research, Springer, vol. 100(1), pages 25-53, December.
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    Cited by:

    1. Michael D. Teter & Johannes O. Royset & Alexandra M. Newman, 2019. "Modeling uncertainty of expert elicitation for use in risk-based optimization," Annals of Operations Research, Springer, vol. 280(1), pages 189-210, September.
    2. Gah-Yi Ban & Jérémie Gallien & Adam J. Mersereau, 2019. "Dynamic Procurement of New Products with Covariate Information: The Residual Tree Method," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 798-815, October.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Yunxiao Deng & Suvrajeet Sen, 2022. "Predictive stochastic programming," Computational Management Science, Springer, vol. 19(1), pages 65-98, January.
    5. Ignacio Rios & Andres Weintraub & Roger J.-B. Wets, 2016. "Building a stochastic programming model from scratch: a harvesting management example," Quantitative Finance, Taylor & Francis Journals, vol. 16(2), pages 189-199, February.
    6. Feng, Yonghan & Ryan, Sarah M., 2016. "Day-ahead hourly electricity load modeling by functional regression," Applied Energy, Elsevier, vol. 170(C), pages 455-465.
    7. Didem Sarı Ay & Sarah M. Ryan, 2019. "Observational data-based quality assessment of scenario generation for stochastic programs," Computational Management Science, Springer, vol. 16(3), pages 521-540, July.

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