Predicting the state of synchronization of financial time series using cross recurrence plots

Mostafa Shabani, Martin Magris, George Tzagkarakis, Juho Kanniainen, Alexandros Iosifidis

Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

Abstrakti

Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross recurrence plot analysis as a nonlinear method for quantifying the multidimensional coupling in the time domain of two time series and for determining their state of synchronization. We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state based on features extracted from dynamically sub-sampled cross recurrence plots. We provide extensive experiments on several stocks, major constituents of the S &P100 index, to empirically validate our approach. We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance (84% F1-score, on average).

AlkuperäiskieliEnglanti
Sivut18519-18531
Sivumäärä13
JulkaisuNeural Computing and Applications
Vuosikerta35
Numero25
DOI - pysyväislinkit
TilaJulkaistu - syysk. 2023
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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