End-to-End Neural Decomposition with Koopman Operators for Time-Series Forecasting
Koopman theory offers a linear-operator view of nonlinear sequence dynamics by lifting observations into a space where evolution is governed by a linear time-invariant Koopman operator. While the Koopman operator provides a linear representation of nonlinear dynamics, it is generally infinite dimensional and defined under time-invariant assumptions. To model non-stationary signals with frequency-dependent behavior, a frequency-varying extension is required. In recent years, deep learning has been increasingly employed to exploit its powerful function-approximation ability for learning the Koopman operator. In this study, we propose a novel approach called neural decomposition Koopman (NDK...
| Authors | De-Yan Lu, Xugang Lu, Yu Tsao et al. |
|---|---|
| Primary category | eess.SP |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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