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Modelling and Forecasting of Network Time Series

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Stochastic Processes in Communication Sciences

This talk concerns the modelling and forecasting of data acquired on the nodes of a network observed through time. Many data sets exist that are acquired directly on the nodes of a network, or there is often an implicit network that be identified or constructed. An important challenge for modelling network data is taking proper account of the high-dimensional inter-node distributional associations, as well as modelling these through time. We present a novel technique for dimension reduction using the network version of the recently devloped “lifting one coefficient at a time” transform which exhibits excellent decorrelation properties both in space and in time. We explain the method of dimension reduction and demonstrate its utility on data arising from epidemiology and wind energy.

This talk is part of the Isaac Newton Institute Seminar Series series.

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