Multi-Parallel Quantum LSTM Improves Forecasting Of High-Dimensional Spatial Time-Series Data

Multi-Parallel Quantum LSTM Improves Forecasting Of High-Dimensional Spatial Time-Series Data

This research presents a new forecasting method that accurately predicts complex, high-dimensional spatial data by strategically selecting key locations, leveraging enhanced quantum-inspired recurrent neural networks, and achieving a root mean squared percentage error of just 0.