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Recalculate Without Recomputing

Author

Listed:
  • José Dias Curto

    (Instituto Universitário de Lisboa (ISCTE-IUL)
    Complexo INDEG/ISCTE)

Abstract

The available memory on a desktop or laptop computer can easily be exceeded by a large data set/stream when performing analytics or training machine learning algorithms. In this paper we propose simple methods to recalculate and update the value of descriptive statistics (averages, variance, coefficients of skewness and kurtosis) after rescaling, adding or excluding some observations. The coefficient of determination from regression analysis is also recalculated after deleting each sample observation. The objective is to avoid including all the observations (especially in a big data set/stream) when those statistics are recalculated. For this, only its previous value updated by the changed observations is needed, saving time in the respective calculation . Applications include GDP, stock prices of the ‘Magnificent Seven’ and Price-to-Earnings ratio.

Suggested Citation

  • José Dias Curto, 2024. "Recalculate Without Recomputing," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3279-3294, December.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:6:d:10.1007_s10614-024-10558-9
    DOI: 10.1007/s10614-024-10558-9
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    References listed on IDEAS

    as
    1. Curto, José Dias & Serrasqueiro, Pedro, 2022. "Averaging financial ratios," Finance Research Letters, Elsevier, vol. 48(C).
    2. Christopher Ferrall, 2005. "Solving Finite Mixture Models: Efficient Computation in Economics Under Serial and Parallel Execution," Computational Economics, Springer;Society for Computational Economics, vol. 25(4), pages 343-379, June.
    3. José Dias Curto, 2022. "Averages: There is Still Something to Learn," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 755-779, August.
    Full references (including those not matched with items on IDEAS)

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