Multi Step

Multi-step methods address the challenge of predicting or planning across multiple sequential steps, a crucial aspect in diverse fields ranging from time series forecasting to program synthesis. Current research focuses on improving accuracy and robustness by employing ensemble methods, advanced neural network architectures (like Transformers and recurrent networks), and integrating external knowledge sources (e.g., weather data, knowledge graphs). These advancements enhance the reliability and interpretability of multi-step predictions, impacting areas such as energy management, process engineering, and scientific discovery through improved forecasting and decision-making capabilities.