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Recursive and direct multi-step forecasting: the best of both worlds

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  • Souhaib Ben Taieb
  • Rob J Hyndman

Abstract

We propose a new forecasting strategy, called rectify, that seeks to combine the best properties of both the recursive and direct forecasting strategies. The rationale behind the rectify strategy is to begin with biased recursive forecasts and adjust them so they are unbiased and have smaller error. We use linear and nonlinear simulated time series to investigate the performance of the rectify strategy and compare the results with those from the recursive and the direct strategies. We also carry out some experiments using real world time series from the M3 and the NN5 forecasting competitions. We find that the rectify strategy is always better than, or at least has comparable performance to, the best of the recursive and the direct strategies. This finding makes the rectify strategy very attractive as it avoids making a choice between the recursive and the direct strategies which can be a difficult task in real-world applications.

Suggested Citation

  • Souhaib Ben Taieb & Rob J Hyndman, 2012. "Recursive and direct multi-step forecasting: the best of both worlds," Monash Econometrics and Business Statistics Working Papers 19/12, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2012-19
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    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp19-12.pdf
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    References listed on IDEAS

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    2. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218.
    3. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    4. Rong Chen & Lijian Yang & Christian Hafner, 2004. "Nonparametric multistep‐ahead prediction in time series analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 669-686, August.
    5. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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    Cited by:

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    2. Ding, Lin & Bai, Yulong & Liu, Ming-De & Fan, Man-Hong & Yang, Jie, 2022. "Predicting short wind speed with a hybrid model based on a piecewise error correction method and Elman neural network," Energy, Elsevier, vol. 244(PA).
    3. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    4. Manuel Paquette-Dupuis & Dalibor Stevanovic & Rachidi Kotchoni, 2019. "Prévisions de l’activité économique en temps de crise," CIRANO Project Reports 2019rp-04, CIRANO.
    5. Amirhossein Sohrabbeig & Omid Ardakanian & Petr Musilek, 2023. "Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess," Forecasting, MDPI, vol. 5(4), pages 1-13, December.
    6. Eran Raviv, 2013. "Prediction Bias Correction for Dynamic Term Structure Models," Tinbergen Institute Discussion Papers 13-041/III, Tinbergen Institute.
    7. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(C).
    8. Meenakshi Narayan & Ann Majewicz Fey, 2020. "Developing a novel force forecasting technique for early prediction of critical events in robotics," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-34, May.
    9. Bartłomiej Gaweł & Andrzej Paliński, 2024. "Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series," Energies, MDPI, vol. 17(2), pages 1-25, January.

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    More about this item

    Keywords

    Multi-step forecasting; forecasting strategies; recursive forecasting; direct forecasting; linear time series; nonlinear time series; M3 competition; NN5 competition;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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