DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting
This addresses forecasting challenges in scientific, industrial, and environmental domains where interpretability and noise robustness are critical, representing a hybrid approach rather than a paradigm shift.
The paper tackles the problem of time series forecasting for high-dimensional nonlinear systems by proposing DeepKoopFormer, which combines Transformers with Koopman operator theory to improve interpretability and stability. The model outperforms LSTM and baseline Transformers in accuracy, robustness to noise, and long-term forecasting stability across synthetic, climate, financial, and electricity datasets.
Time series forecasting plays a vital role across scientific, industrial, and environmental domains, especially when dealing with high-dimensional and nonlinear systems. While Transformer-based 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. In this work, we propose DeepKoopFormer, a principled forecasting framework that combines the representational power of Transformers with the theoretical rigor of Koopman operator theory. Our model features a modular encoder-propagator-decoder structure, where temporal dynamics are learned via a spectrally constrained, linear Koopman operator in a latent space. We impose structural guarantees-such as bounded spectral radius, Lyapunov based energy regularization, and orthogonal parameterization to ensure stability and interpretability. Comprehensive evaluations are conducted on both synthetic dynamical systems, real-world climate dataset (wind speed and surface pressure), financial time series (cryptocurrency), and electricity generation dataset using the Python package that is prepared for this purpose. Across all experiments, DeepKoopFormer consistently outperforms standard LSTM and baseline Transformer models in terms of accuracy, robustness to noise, and long-term forecasting stability. These results establish DeepKoopFormer as a flexible, interpretable, and robust framework for forecasting in high dimensional and dynamical settings.