GRU with and without Decomposition: Prediction Performance Comparison

This paper explores the prediction performance of Gated Recurrent Units (GRUs) with and without decomposition. GRUs are a type of recurrent neural network (RNN) known for their ability to learn long-term dependencies in sequential data. Decomposition techniques, such as the ones used in the Long Short-Term Memory (LSTM) network, aim to improve the GRU's ability to capture complex patterns and long-term dependencies. The study compares the predictive capabilities of GRUs with and without decomposition on various datasets and tasks, analyzing the impact of decomposition on prediction accuracy, model complexity, and training efficiency.