Publication Details |
| Category | Text Publication |
| Reference Category | Preprints |
| DOI | 10.48550/arXiv.2505.20048 |
Licence  |
|
| Title (Primary) | Synthetic time series forecasting with transformer architectures: extensive simulation benchmarks |
| Author | Forootani, A.; Khosravi, M. |
| Source Titel | arXiv |
| Year | 2025 |
| Department | BIOENERGIE |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Abstract | Time series forecasting plays a critical role in domains such as energy,
finance, and healthcare, where accurate predictions inform
decision-making under uncertainty. Although Transformer-based models
have demonstrated success in sequential modeling, their adoption for
time series remains limited by challenges such as noise sensitivity,
long-range dependencies, and a lack of inductive bias for temporal
structure. In this work, we present a unified and principled framework
for benchmarking three prominent Transformer forecasting
architectures-Autoformer, Informer, and Patchtst-each evaluated through
three architectural variants: Minimal, Standard, and Full, representing
increasing levels of complexity and modeling capacity.
We conduct over 1500 controlled experiments on a suite of ten synthetic signals, spanning five patch lengths and five forecast horizons under both clean and noisy conditions. Our analysis reveals consistent patterns across model families. To advance this landscape further, we introduce the Koopman-enhanced Transformer framework, Deep Koopformer, which integrates operator-theoretic latent state modeling to improve stability and interpretability. We demonstrate its efficacy on nonlinear and chaotic dynamical systems. Our results highlight Koopman based Transformer as a promising hybrid approach for robust, interpretable, and theoretically grounded time series forecasting in noisy and complex real-world conditions. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30844 |
| Forootani, A., Khosravi, M. (2025): Synthetic time series forecasting with transformer architectures: extensive simulation benchmarks arXiv 10.48550/arXiv.2505.20048 |
|