Interpretable deep convolutional model for nonlinear multivariate time series in complex systems

arXiv:2501. 04339v2 Announce Type: replace Abstract: We introduce the Deep Convolutional Interpreter for Time Series (DCIts), a deep-learning architecture for nonlinear multivariate time series that provides sample-specific, locally interpretable descriptions of the underlying interaction structure.