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

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