###### Abstract  
Dams are one of the most important engineering works of the current human society, and it is crucial to monitor and obtain analytical data to log their conditions, predict their behavior and, eventually, receive early warnings for planning interventions and maintenance activities. In this context, GNSS-based point displacement monitoring is nowadays a consolidated technique that is able to provide daily millimeter level accuracy, even with less sophisticated and less expensive single-frequency equipment. If properly designed, daily records of such monitoring systems produce time series that, when long enough, allow for an accurate reconstruction of the geometrical deformation of the structure, thus guiding semi-automatic early warning systems. This paper focuses on the procedure for the GNSS time series processing with a statistical approach. In particular, real-world times series collected from a dam monitoring test case are processed as an example of data filtering. A remove-restore technique based on a collocation approach is applied here. Basically, it consists of an initial deterministic modeling by polynomials and periodical components through least squares adjustment and Fourier transform, respectively, followed by a stochastic modeling based on empirical covariance estimation and a collocation approach. Filtered time series are interpreted by autoregressive models based on environmental factors such as air or water temperature and reservoir water level. Spatial analysis is finally performed by computing correlations between displacements of the monitored points, as well as by visualizing the overall structure deformation in time. Results positively validate the proposed data processing workflow, providing useful hints for the implementation of automatic early warning systems in the framework of structural monitoring based on continuous displacement measurements. s 2022 20  
predict their behavior and, eventually, receive early warnings for planning interventions and maintenance activities. The failure of dams, although rare, can be catastrophic and may cause immense damage and loss of life [4]. The most extreme example is the sequential failure in 1975 of the Banqiao Reservoir Dam and more than 60 others in Henan Province, China. Dramatically, the official number of victims caused by this event was 26,000 [5]. Dam health monitoring plays an important role in securing their structural integrity and maintaining their longevity. It calls for the use of multiple sensors for monitoring a wide variety of quantities such as body deformation, point displacements, temperature, uplift and pore pressures, seepage and drainage rates, and piezometric level, just to cite the most significant ones [6]. Regarding the measurement of displacements and deformation, i.e., the geometric variation of the dam and its surroundings, the typical approach consists of monitoring a set of Control Points (CPs) established properly in key positions (e.g., the dam crest, the downstream banks, the lateral abutments) to retrieve the general behavior of the whole structure. Several techniques have been developed and applied by exploiting technological advancements that brought innovation and enhancements in terms of acquisition rate, spatial coverage, accuracy, and automation. Since the beginning of the 20 th century, the static monitoring of dams was mainly based on geodetic control networks by applying classical techniques such as precision triangulation and geometric leveling [7; 8]. Such measurements of absolute and relative displacements of the structure and of the nearby areas were often complemented or integrated with other measurement methods based on geotechnics and structural sensors, e.g., direct and inverted pendulums, rock meters, tiltmeters, strain meters and clinometers [9; 10; 11].