Step Prediction

Step prediction, the forecasting of future states or events based on past observations, aims to improve accuracy and efficiency across diverse fields. Current research emphasizes multi-step prediction using various deep learning architectures, including transformers, recurrent neural networks (RNNs like LSTMs and GRUs), and novel hybrid models combining these approaches, often incorporating techniques like ensemble methods and iterative decoding to mitigate error accumulation. These advancements are impacting diverse applications, from financial market forecasting and weather prediction to autonomous driving and healthcare, enabling more accurate and timely decision-making in complex systems.