3 papers
cs.LG2026
Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess +2
Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial dema…
cs.LG2026
Detecting Invariant Manifolds in ReLU-Based RNNs
Lukas Eisenmann, Alena Brändle, Zahra Monfared +1
Recurrent Neural Networks (RNNs) have found widespread applications in machine learning for time series prediction and dynamical systems reconstruction, and experienced a recent re…
cs.LG2026
Continuous-Time Piecewise-Linear Recurrent Neural Networks
Alena Brändle, Alena Brändle, Lukas Eisenmann +3
In dynamical systems reconstruction (DSR) we aim to recover the dynamical system (DS) underlying observed time series. Specifically, we aim to learn a generative surrogate model wh…