Short Term

Short-term analysis focuses on understanding and predicting phenomena within limited timeframes, contrasting with long-term perspectives. Current research emphasizes developing models that effectively integrate short-term information with longer-term trends, often employing techniques like sliding window approaches, attention mechanisms (e.g., long-short term memory networks, transformers), and generative adversarial networks. These advancements are crucial across diverse fields, improving predictions in areas such as stock markets, recommender systems, and real-time action recognition, while also enhancing the robustness of models to noise and uncertainty.