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Earthquakes, snow avalanches, and landslides are well-known phenomena that often lead to wide-scale destruction. We will concentrate on a small-scale system that exhibits stick-slip dynamics closely connected to the avalanches. In particular, we will consider a system of granular particles confined by two walls. The top wall of the system is pulled by a spring moving with constant velocity. This external forcing exposes the particles to time-dependent shear stress. In the stick-slip regime, the force network, describing the state of the system, evolves slowly and the top wall remains at rest until the spring force becomes sufficiently large. At this point, the wall slips, and an abrupt rearrangement of the force network occurs. In this talk, we will show that the upcoming slip events can be predicted by combining the methods of topological data analysis and Bayesian statistics. We will represent the time evolution of the force network as a time series in the space of persistence diagrams and find relevant descriptors in this space that are amenable to the Bayesian analysis.