Modern High Performance Computing (HPC) storage systems use heterogeneous storage technologies organized in tiers to find a compromise between capacity, performance, and cost. In these systems, prefetching is a common technique used to move the right data at the right moment from a slow to a fast tier to improve the overall performance while using the costly high-performance tier only when needed. Effective prefetching requires precise knowledge of the application I/O patterns. This knowledge can be extracted through the source code, I/O tracing tools or I/O functions call stacks. State-of-the-art solutions based on the latter approach mainly focus on applications with regular I/O profiles to avoid scalability issues due to the grammar-based techniques used. In this paper, we present an approach based on I/O call stacks that models I/O patterns for both regular and irregular applications, thanks to the use of directed graphs. We present different models for prefetching. Our models were used to predict the next I/O call stack on two real HPC applications and one synthetic workload with an accuracy of up to 98%, while keeping a low overheadt.