Given that real-time systems are specified to a degree of confidence, budget overruns should be expected to occur in a system at some point. When a budget overrun occurs, it is necessary to understand how long such a state persists, in order to determine if the fault tolerance of the system is adequate to handle the problem. However, given the rarity of budget overruns in testing, it cannot be assumed that sufficient data will be available to build an accurate model. Hence this paper presents a new application of Markov Chain based modelling techniques combined with forecasting techniques to determine an appropriate fault model, using Lossy Compression to fit the model to the available data. In addition, a new algorithm, DepET, for generating job execution times with dependencies is given for use in task simulators.