Novel long-memory spectral domain modelling for network data

Novel long-memory spectral domain modelling for network data Cover image

In traditional data analysis, spectral methods have been shown to offer a complementary view to time domain analysis, often elucidating different information by identifying the frequencies or scales at which the important structure or dynamic behaviour occurs. In particular, long-range (persistent) dependence can be more robustly characterised and estimated in the spectral domain, leading to more interpretable analysis. However, whilst recent work has explored time domain approaches to modelling network data, spectral analysis of network-structured time series has to date been lacking. This project aims to create a suite of tools to fill this gap in the network literature encompassing the modelling of processes that display stationary behaviour with potentially both short and long-memory components, as well as estimation and inference frameworks for these components. We anticipate impact to be generated in the following application areas: biology and neuroscience, finance and cybersecurity. 

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