Cyclic behaviour over a range of timescales is a well-documented feature of many dome-forming volcanoes, but has not previously been identified in high resolution seismic data from Volcán de Colima (Mexico). Using daily seismic count datasets from Volcán de Colima and Soufrière Hills volcano (Montserrat), this study explores parallels in the long-term behaviour of seismicity at two long-lived systems. Datasets are examined using multiple techniques, including Fast-Fourier Transform, Detrended Fluctuation Analysis and Probabilistic Distribution Analysis, and the comparison of results from two systems reveals interesting parallels in sub-surface processes operating at both systems. Patterns of seismicity at both systems reveal complex but broadly similar long-term temporal patterns with cycles on the order of ~ 50- to ~ 200-days. These patterns are consistent with previously published spectral analyses of SO2 flux time-series at Soufrière Hills volcano, and are attributed to variations in the movement of magma in each system. Detrended Fluctuation Analysis determined that both volcanic systems showed a systematic relationship between the number of seismic events and the relative ‘roughness’ of the time-series, and explosions at Volcán de Colima showed a 1.5–2 year cycle; neither observation has a clear explanatory mechanism. At Volcán de Colima, analysis of repose intervals between seismic events shows long-term behaviour that responds to changes in activity at the system. Similar patterns for both volcanic systems suggest a common process or processes driving the observed signal but it is not clear from these results alone what those processes may be. Further attempts to model conduit processes at each volcano must account for the similarities and differences in activity within each system. The identification of some commonalities in the patterns of behaviour during long-lived dome-forming eruptions at andesitic volcanoes provides a motivation for investigating further use of time-series analysis as a monitoring tool.