Trend Analysis

Trend analysis focuses on identifying patterns and changes over time within various data types, aiming to understand underlying dynamics and make predictions. Current research emphasizes the development of robust and efficient methods for detecting trends, particularly in complex, high-dimensional data, employing techniques like reinforcement learning, time series analysis (including ARIMA and GCNs), and large language models for text analysis. These advancements have significant implications across diverse fields, improving forecasting accuracy in areas such as finance, healthcare (e.g., blood glucose prediction), and environmental monitoring (e.g., air pollution), as well as enabling more efficient knowledge organization and retrieval in scientific literature.