Traditional Recurrent Neural Network

Traditional recurrent neural networks (RNNs), such as LSTMs and GRUs, process sequential data by maintaining an internal "memory" of past inputs. Recent research focuses on overcoming RNNs' limitations, particularly their sequential processing which hinders parallelization and scalability, by exploring novel architectures and algorithms that improve training efficiency and long-term memory capabilities, including modifications to existing RNNs and entirely new approaches like RWKV-TS. This renewed interest stems from the need for efficient and powerful models for various applications, including time series forecasting and natural language processing, where RNNs' inherent strengths in handling sequential dependencies remain valuable.