Fractional differentiation and its use in machine learning

Janusz Gajda () and Rafał Walasek ()
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Janusz Gajda: Faculty of Economic Sciences, University of Warsaw
Rafał Walasek: Faculty of Economic Sciences, University of Warsaw

No 2020-32, Working Papers from 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.

Keywords: fractional differentiation; financial time series; stock exchange; artificial neural networks (search for similar items in EconPapers)
JEL-codes: C22 C32 G10 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets and nep-ore
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https://www.wne.uw.edu.pl/index.php/download_file/5878/ First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-32

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