Sequence Prediction

Sequence prediction, the task of forecasting future elements in a series, aims to understand and model the underlying patterns governing sequential data across diverse domains. Current research emphasizes efficient algorithms like convolutional and spectral transform units, alongside the continued exploration and refinement of transformer-based architectures, often leveraging techniques like pre-training and self-supervised learning to improve accuracy and address issues like fairness in applications. This field is crucial for advancing numerous applications, from improving healthcare recommendations and weather forecasting to enhancing natural language processing and robotics, by enabling more accurate and efficient predictions in complex systems.