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Time series forecasting : a test of automated econometric methods

Author

Listed:
  • Erick Inácio Ferreira

    (UFMG)

  • Igor Viveiros Melo Souza

    (UFMG)

Abstract

The aim of this study is to assess the performance of two well-known algorithms which automate the process of modeling and forecasting time series, each applying a different econometric technic: ARIMA or exponential smoothing. We provide a brief discussion of how these algorithms work and results of a Monte Carlo experiment, which was conducted to evaluate the capabilities of auto.arima and ets, available in Rob Hyndman’s forecast package for the statistical software R, commonly used by economists to study and forecast time series. Over 200.000 synthetic series were simulated, with several different characteristics, used to test both methods and report metrics of correct modeling and out-of-sample forecast errors of the algorithms, on top of which we provide a brief discussion of the successes and shortcomings that happened while applying each algorithm.

Suggested Citation

  • Erick Inácio Ferreira & Igor Viveiros Melo Souza, 2023. "Time series forecasting : a test of automated econometric methods," Textos para Discussão Cedeplar-UFMG 661, Cedeplar, Universidade Federal de Minas Gerais.
    • Handle: RePEc:cdp:texdis:td661
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    File URL: https://www.cedeplar.ufmg.br/pesquisas/td/TD%20661.pdf
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    References listed on IDEAS

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    1. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    5. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    6. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
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