Abstract: Longitudinal data have attracted considerable attention in fields such as public health because they capture the dynamic trajectories of the same subjects over time.Data cleaning process ensures the quality of longitudinal data modelling and directly influences downstream analysis quality.To address this issue,this paper proposes a novel anomaly detection method which integrates generalized linear model(GLM)-based polynomial fittingand adaptive clustering.The approach assigns binary normal and abnormal labels to individual trajectories and compares them with ground-truth annotations.On four independent datasets(two simulated longitudinal cohorts,one additional UCR dataset,and one real-world clinical dataset) a systematically comparison against established R-package methods is conducted.Experimental results demonstrate superior detection performance and robust generalizability across diverse settings.Applying the proposed method to six years of height data from primary school students in a specific city further demonstrates its effectiveness and accuracy in detecting outlier trajectories in practical longitudinal health data.This method offers robust support for public health surveillance and intervention,disease progression assessment,and clinical decision support.

Key words: Longitudinal trajectory, Trajectory anomalies detection, Data cleaning, Unsupervised clustering