Global Temporal

Global temporal analysis focuses on understanding and leveraging the temporal dependencies within data across its entire duration, aiming for improved accuracy and interpretability in various applications. Current research emphasizes developing models that effectively capture both local and global temporal features, employing techniques like convolutional neural networks, transformers, and hyperdimensional computing within architectures such as diffusion models and spiking neural networks. These advancements are improving performance in diverse fields, including time series imputation, load forecasting, human motion capture, and action detection, by enabling more accurate and efficient processing of complex temporal patterns. The resulting improvements in accuracy and interpretability have significant implications for numerous scientific and engineering domains.