DeepKoopFormer: a Koopman enhanced transformer based architecture for time series forecasting

Abstract Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Transformer models have recently achieved state-of-the-art performance in long-range forecasting, they often suffer from interpretability issues and instability in the presence of noise or dynamical uncertainty. We propose , a forecasting framework that combines Transformer-based sequence modeling with Koopman-ins