Yu Tsao

End-to-End Neural Decomposition with Koopman Operators for Time-Series Forecasting

This study proposes an end-to-end architecture that integrates a learnable signal decomposition module with both frequency-independent and frequency-dependent Koopman based networks for sequence forecasting and demonstrates that decomposing a signal into a frequency-independent trend component and a frequency-dependent periodic component improves prediction accuracy when perfect linearization is unattainable.