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Abstract:
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Network based approaches for analyses of time series data can provide new insights concerning causality and forecasting. However, it is challenging to construct such networks using time series data, due to correlations and autocorrelations between the nodes. In this presentation, we describe a new method of construction of an implicit network, which is necessarily multilayer, and provide various network wide metrics (NWM) that are useful for identifying the active features of the multilayer network. We study the asymptotic properties of NWM when the number of nodes, features, and the sample size increase. Finally, we utilize these asymptotic properties to identify communities within the implicit multilayer network.
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