The research presents semi-automatic method for anomaly detection in GNSS time series using Isolation Forest algorithm including detailed method stages as well as chosen parameters of algorithm. The method including data collection and preprocessing, isolation forest model creation and training, anomaly detection as well as results visualization are fully implemented in Python. The method includes two options for GNSS time series analysis. First include the analysis of separate time series on local anomalies both in vertical and horizontal (both North and East) components. Second allows to identify simultaneous group anomalies in the network of GNSS stations. As an example, the method is tested on 37 time series of daily solutions of Geoterrace GNSS network, Ukraine. Some detected semi-automatic anomalies coincide with those detected in previous studies such as non-tidal atmospheric loading as well as some expluatetion changes confirmed by stations log-files. The rest anomalies will require detailed consideration by geophysicists in further research.
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