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Multi-period forecasting and scenario generation with limited data

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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.

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Notes

  1. Usually one described by a system of stochastic differential equations (Wiener, Lévy, \(\dots \)) or by a discrete time series (ARMA, VARMA, \(\dots \)).

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Author notes

    Authors and Affiliations

    1. University of Chile, Santiago, Chile

      Ignacio Rios

    2. Mathematics Department, UC Davis, Davis, CA, 95616, USA

      Roger J-B Wets

    3. GSM, UC Davis, Davis, CA, 95616, USA

      David L. Woodruff

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    1. Ignacio Rios
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    2. Roger J-B Wets
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    3. David L. Woodruff
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    Corresponding author

    Correspondence to David L. Woodruff.

    Additional information

    R. J-B. Wets contributions were partially supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under grant W911NF-12-1-0273.

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    Rios, I., Wets, R.JB. & Woodruff, D.L. Multi-period forecasting and scenario generation with limited data. Comput Manag Sci 12, 267–295 (2015). https://doi.org/10.1007/s10287-015-0230-5

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    • DOI: https://doi.org/10.1007/s10287-015-0230-5

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