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Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots. (arXiv:2210.14605v2 [q-fin.ST] UPDATED)
Nov. 3, 2022, 1:13 a.m. | Mostafa Shabani, Martin Magris, George Tzagkarakis, Juho Kanniainen, Alexandros Iosifidis
stat.ML updates on arXiv.org arxiv.org
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 …
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