Spatiotemporal Sequence

Spatiotemporal sequence modeling focuses on analyzing and predicting data that changes over both space and time, encompassing diverse applications from weather forecasting to human activity recognition. Current research emphasizes developing efficient and accurate models, often employing convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs) like LSTMs, sometimes augmented with physically-inspired components like advection operators or graph structures to capture complex relationships within the data. These advancements aim to improve prediction accuracy, particularly for long-range forecasting and chaotic systems, while also addressing computational limitations through techniques like parallel processing and model compression. The resulting improvements have significant implications across various fields, enabling more accurate predictions and deeper understanding of dynamic processes.