# Estimation of Correlation Matrices from Limited time series Data using Machine Learning

arXiv: https://arxiv.org/abs/2209.01198 · HTML: https://mlsanity.com/paper/2209.01198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 2 Sep 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Anticipating synchronization with machine learning](https://mlsanity.com/paper/2103.13358/) (2021)
- [BETA: A Large Benchmark Database Toward SSVEP-BCI Application](https://mlsanity.com/paper/1911.13045/) (2019)
- [A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction](https://mlsanity.com/paper/2008.01645/) (2020)
- [Identification of Chimera using Machine Learning](https://mlsanity.com/paper/2001.08985/) (2020)
- [Machine learning dynamical phase transitions in complex networks](https://mlsanity.com/paper/1911.04633/) (2019)
- [From Correlation to Causation: Estimation of Effective Connectivity from Continuous Brain Signals based on Zero-Lag Covariance](https://mlsanity.com/paper/1708.02423/) (2017)

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

- [Master Stability Functions in Complex Networks](https://mlsanity.com/paper/2412.19163/) (2024)

## Abstract (arXiv)

Correlation matrices contain a wide variety of spatio-temporal information about a dynamical system. Predicting correlation matrices from partial time series information of a few nodes characterizes the spatio-temporal dynamics of the entire underlying system. This information can help to predict the underlying network structure, e.g., inferring neuronal connections from spiking data, deducing causal dependencies between genes from expression data, and discovering long spatial range influences in climate variations. Traditional methods of predicting correlation matrices utilize time series data of all the nodes of the underlying networks. Here, we use a supervised machine learning technique to predict the correlation matrix of entire systems from finite time series information of a few randomly selected nodes. The accuracy of the prediction validates that only a limited time series of a subset of the entire system is enough to make good correlation matrix predictions. Furthermore, using an unsupervised learning algorithm, we furnish insights into the success of the predictions from our model. Finally, we employ the machine learning model developed here to real-world data sets.