Throughout the earthquake cycling at fault zones, Earth’s crust undergoes deformations. GNSS coordinate time series record linear tectonic motion, seismic displacements, postseismic decays, and periodic signatures such as non-tidal loading. Any additional motions can be classed as transient tectonic signals, i.e., unexpected accelerations with respect to the standard trajectory model. As the number of permanent stations increases and as time series grow, we are increasingly able to recognise transient tectonic signals. Since some of these suspected tectonic transients have subtle magnitudes or sometimes unusual spatiotemporal features, we need to develop methods for determining which transients are artifacts and which are of tectonic origin. Here, we investigate the impact of certain GNSS processing choices and how they affect the appearance of transients in the GNSS displacement time series solutions. In this study, we choose data from Cascadia, a region for which the occurrence of transient signals in the GNSS time series is well known. We processed data based from 189 selected stations in network mode for the time span 2015 to 2019. After producing coordinate time series, we then built a pipeline to isolate processing artifacts and tectonic transients, using the regression model-based algorithm known as GrAtSiD (Greedy Automatic Signal Decomposition). The residuals of GNSS observations show that most sites have a precision of 5 mm to 10 mm. Using the GrAtSiD algorithm, we detected transient signals with velocities exceeding 0.3 mm/day near the ALBH station.