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Dynamic causal networks with multi-scale temporal structure

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SNAW04 - Dynamic Networks

Co-authors: Xinyu Kang (Boston University), Apratim Ganguly (Boston University)

I will discuss a novel method to model multivariate time series using dynamic causal networks. This method combines traditional multi-scale modeling and network based neighborhood selection, aiming at capturing the temporally local structure of the data while maintaining the sparsity of the potential interactions. Our multi-scale framework is based on recursive dyadic partitioning, which recursively partitions the temporal axis into finer intervals and allows us to detect local network structural changes at varying temporal resolutions. The dynamic neighborhood selection is achieved through penalized likelihood estimation, where the penalty seeks to limit the number of neighbors used to model the data. Theoretical and numerical results describing the performance of our method will be presented, as well as an application in computational neuroscience. 

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

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