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Note---Sliding Simulation: A New Approach to Time Series Forecasting

Author

Listed:
  • Spyros Makridakis

    (INSEAD, Boulevard de Constance, 77305 Fontainebleau, France)

Abstract

This paper proposes a new approach to time series forecasting based upon three premises. First, a model is selected not by how well it fits historical data but on its ability to accurately predict out-of-sample actual data. Second, a model/method is selected among several run in parallel using out-of-sample information. Third, models/methods are optimized for each forecasting horizon separately, making it possible to have different models/methods to predict each of the m horizons. This approach outperforms the best method of the M-Competition by a large margin when tested empirically with the 111 series subsample of the M-Competition data.

Suggested Citation

  • Spyros Makridakis, 1990. "Note---Sliding Simulation: A New Approach to Time Series Forecasting," Management Science, INFORMS, vol. 36(4), pages 505-512, April.
    • Handle: RePEc:inm:ormnsc:v:36:y:1990:i:4:p:505-512
    DOI: 10.1287/mnsc.36.4.505
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    Cited by:

    1. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    2. Hibon, Michele & Evgeniou, Theodoros, 2005. "To combine or not to combine: selecting among forecasts and their combinations," International Journal of Forecasting, Elsevier, vol. 21(1), pages 15-24.
    3. Corredera, Alberto & Ruiz, Carlos, 2023. "Prescriptive selection of machine learning hyperparameters with applications in power markets: Retailer’s optimal trading," European Journal of Operational Research, Elsevier, vol. 306(1), pages 370-388.
    4. Bacci, Livio Agnew & Mello, Luiz Gustavo & Incerti, Taynara & Paulo de Paiva, Anderson & Balestrassi, Pedro Paulo, 2019. "Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated fact," International Journal of Production Economics, Elsevier, vol. 212(C), pages 186-211.
    5. Corredera, Alberto, 2022. "Prescriptive selection of machine learning hyperparameters with applications in power markets: retailer's optimal trading," DES - Working Papers. Statistics and Econometrics. WS 33693, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Tashman, Leonard J. & Kruk, Joshua M., 1996. "The use of protocols to select exponential smoothing procedures: A reconsideration of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 12(2), pages 235-253, June.
    7. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.

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