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A pragmatic view of accuracy measurement in forecasting

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  • Flores, Benito E

Abstract

Accuracy measurement in forecasting is always a subject of debate because of its importance. An adequate metric is necessary to properly select a forecasting method for a specific application. Competitions to determine the best method have helped the practitioner. The criteria for selection have not received as much attention. Of the two kinds of measurement statistics--relative and absolute--the former may present problems for the user if zeros or near zero values appear. This is more a practitioner problem because artificially generated time series do not usually have zeros. The relative and absolute measures are discussed and a solution for the existence of zeros in the data is given. If symmetry of the errors is a problem solutions are discussed. Managers will select the metric depending on the application and their management style. Once the metric has been selected the decision as to which forecasting method to select in a given situation becomes a less difficult problem.

Suggested Citation

  • Flores, Benito E, 1986. "A pragmatic view of accuracy measurement in forecasting," Omega, Elsevier, vol. 14(2), pages 93-98.
  • Handle: RePEc:eee:jomega:v:14:y:1986:i:2:p:93-98
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    Cited by:

    1. Tsusaka, Takuji W. & Velasco, Ma. Lourdes & Yamano, Takashi & Pandey, Sushil, 2015. "Expert Elicitation for Assessing Agricultural Technology Adoption: The Case of Improved Rice Varieties in South Asian Countries," Asian Journal of Agriculture and Development, Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA), vol. 12(1), pages 1-15, June.
    2. B. Shravan Kumar & Vadlamani Ravi & Rishabh Miglani, 2019. "Predicting Indian stock market using the psycho-linguistic features of financial news," Papers 1911.06193, arXiv.org.
    3. Seyma Caliskan Cavdar & Alev Dilek Aydin, 2015. "An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 8(3), pages 1-18, August.
    4. Rahman A. Prasojo & Karunika Diwyacitta & Suwarno & Harry Gumilang, 2017. "Transformer Paper Expected Life Estimation Using ANFIS Based on Oil Characteristics and Dissolved Gases (Case Study: Indonesian Transformers)," Energies, MDPI, Open Access Journal, vol. 10(8), pages 1-18, August.
    5. Williams, Dan W. & Miller, Don, 1999. "Level-adjusted exponential smoothing for modeling planned discontinuities1," International Journal of Forecasting, Elsevier, vol. 15(3), pages 273-289, July.
    6. Canelles, Q. & AquiluƩ, N. & Duane, A. & Brotons, L., 2019. "From stand to landscape: modelling post-fire regeneration and species growth," Ecological Modelling, Elsevier, vol. 404(C), pages 103-111.
    7. Evgeny A. Antipov & Elena B. Pokryshevskaya, 2017. "Are box office revenues equally unpredictable for all movies? Evidence from a Random forest-based model," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(3), pages 295-307, June.
    8. Nailya Maitanova & Jan-Simon Telle & Benedikt Hanke & Matthias Grottke & Thomas Schmidt & Karsten von Maydell & Carsten Agert, 2020. "A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports," Energies, MDPI, Open Access Journal, vol. 13(3), pages 1-23, February.

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