Exploring New Paradigms in Time Series Prediction by Integrating Computer Simulations and Machine Learning

Time series prediction is a challenging task that requires modeling complex temporal dependencies and structural priors. In this work, we propose a novel framework that fundamentally advances existing Transformer-based methods through the introduction of the Dynamic Temporal Attention Network (DTAN) and the Hierarchical Temporal Optimization Strategy (HTOS), which jointly address long-term dependency capture, interpretability, and domain-specific adaptation in a unified architecture. To address existing limitations, we propose a novel paradigm that integrates machine learning with computer simulations by embedding simulation-derived constraints and structural priors directly into the model training process. These simulations serve not merely as synthetic data generators but as sources of domain-specific knowledge, including physics-informed constraints and governing equations, which are used to guide attention mechanisms and regularization strategies within our architecture. Our framework introduces DTAN, which combines hierarchical temporal encoding with adaptive attention mechanisms to dynamically capture both short- and long-term dependencies. Additionally, we incorporate HTOS, which integrates auxiliary tasks such as trend decomposition and seasonality estimation while enforcing structured learning through hierarchical regularization. By merging data-driven learning with simulation-informed constraints, our approach enhances both predictive accuracy and interpretability. Empirical evaluations across diverse benchmark datasets validate the effectiveness of our framework, demonstrating its robustness, scalability, and ability to uncover meaningful temporal patterns in complex time series data.