# Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots

arXiv: https://arxiv.org/abs/2210.14605 · HTML: https://mlsanity.com/paper/2210.14605/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 73 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods](https://mlsanity.com/paper/1705.03233/) (2017)
- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://mlsanity.com/paper/1712.00975/) (2017)
- [Forecasting with time series imaging](https://mlsanity.com/paper/1904.08064/) (2019)
- [Visual Time Series Forecasting: An Image-driven Approach](https://mlsanity.com/paper/2011.09052/) (2020)
- [Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction](https://mlsanity.com/paper/2201.05459/) (2022)
- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://mlsanity.com/paper/2207.11577/) (2022)
- [Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting](https://mlsanity.com/paper/2107.06995/) (2021)
- [An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework](https://mlsanity.com/paper/1712.09592/) (2017)

## Built on by (most cited first, 1 of 1)

- [Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach](https://mlsanity.com/paper/2409.06728/) (2024)

## Abstract (arXiv)

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.