English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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