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Fractional differentiation and its use in machine learning

Author

Listed:
  • Janusz Gajda

    (Faculty of Economic Sciences, University of Warsaw)

  • Rafał Walasek

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

This article covers the implementation of fractional (non-integer order) differentiation on four datasets based on stock prices of main international stock indexes: WIG 20, S&P 500, DAX, Nikkei 225. This concept has been proposed by Lopez de Prado to find the most appropriate balance between zero differentiation and fully differentiated time series. The aim is making time series stationary while keeping its memory and predictive power. This paper makes also the comparison between fractional and classical differentiation in terms of the effectiveness of artificial neural networks. This comparison is done in two viewpoints: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The conclusion of the study is that fractionally differentiated time series performed better in trained ANN.

Suggested Citation

  • Janusz Gajda & Rafał Walasek, 2020. "Fractional differentiation and its use in machine learning," Working Papers 2020-32, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2020-32
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/5878/
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    References listed on IDEAS

    as
    1. Hedayati , Amin & Hedayati , Moein & Esfandyari, Morteza, 2016. "Stock market index prediction using artificial neural network," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 21(41), pages 89-93.
    2. Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    fractional differentiation; financial time series; stock exchange; artificial neural networks;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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