Abstract: [Purposes] Traditional outlier detection methods process each station and direction independently，ignoring the spatio-temporal cross-correlation of GNSS monitoring data，and thus fail to identify multivariate collaborative anomalies.This paper aims to exploit dynamic couplings among monitoring series to improve such identification. [Methods] This paper develops a vector autoregressive (VAR)model integrating three-dimensional displacements (E，N，U) to analyze dynamic couplings among monitoring series.An adaptive criterion based on model residuals distinguishes true deformation from observation outliers. [Findings] In simulations，the F1-score of gross error detection with the proposed method improved by approximately 20% on average compared with the traditional 3-times mean error method.In field data from 13 stations on the Jiashao Bridge，the average RMSE in the E，N，and U directions decreased by approximately 60%，85%，and 75%，respectively，compared with the original series，and the overall RMSE was significantly lower than that of other traditional methods. [Conclusions] The method significantly improves data quality，providing a better foundation for long-term health monitoring．

Key words: GNSS, gross error detection, vector autoregression, multivariate time series, VAR model