Time Adaptive Gaussian Model
This addresses the challenge of analyzing dynamic connections in time series data for researchers and practitioners in fields like data analysis, though it appears incremental as it builds on existing models.
The authors tackled the problem of inferring time-varying relationships in multivariate time series by proposing the Time Adaptive Gaussian Model (TAGM), which generalizes state-of-the-art methods and reduces assumptions while maintaining good inference performance.
Multivariate time series analysis is becoming an integral part of data analysis pipelines. Understanding the individual time point connections between covariates as well as how these connections change in time is non-trivial. To this aim, we propose a novel method that leverages on Hidden Markov Models and Gaussian Graphical Models -- Time Adaptive Gaussian Model (TAGM). Our model is a generalization of state-of-the-art methods for the inference of temporal graphical models, its formulation leverages on both aspects of these models providing better results than current methods. In particular,it performs pattern recognition by clustering data points in time; and, it finds probabilistic (and possibly causal) relationships among the observed variables. Compared to current methods for temporal network inference, it reduces the basic assumptions while still showing good inference performances.