Trend Filtering

Trend filtering is a nonparametric regression technique designed to extract underlying trends from noisy data by applying varying degrees of smoothness. Current research focuses on enhancing its adaptability to complex data structures, including developing algorithms like min-max trend filtering for local adaptivity and reinforcement learning-based approaches for detecting dynamic trends and abrupt changes. These advancements improve the accuracy and robustness of trend estimation in diverse applications, such as time series analysis, anomaly detection, and graph signal processing, leading to more reliable insights from complex datasets.