CSIS Seminar

Network Analysis for Time Series Data

Abstract

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. Additionally, the problem gets more complicated when exogenous variables are present. In this presentation, we (i) describe theoretical guarantees for the regularization method for estimating the autoregressive parameters and the regression coefficients in an ARX Model, (ii) describe a new method for constructing an implicit network, and (iii) provide various network wide metrics (NWM) that are useful for identifying the active features of the implicit network. We also describe the asymptotic properties of NWM and utilize them to identify communities via the proposed implicit network.

Speaker Bio

TBD