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We introduce simplicial VAR models for time-varying processes observed on higher-order networks. These models adeptly capture intricate spatio-temporal dependencies within simplicial signals while significantly reducing parameter complexity relative to conventional VAR models.
MATLAB iterative algorithm that fits a n-dimensional simplicial complex to a point cloud in arbitrary dimensions based on a generalization of k-means clustering