arXiv Machine Learning June 10, 2026 By Domjan Baric, Davor Horvatic

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

Read the original on arXiv Machine Learning →

arXiv:2501. 04339v2 Announce Type: replace-cross 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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