In the 21st century, generating data has become an efficient alternative to traditional data collection methods. In this poster, we present the motivation and methods behind a new data generation tool capable of taking in a probability model and generating a customized stream of realistic data. Three cases of time series systems are considered. The static case type implies that the events at a given time t are independent of events at all other time steps—time is effectively irrelevant for data generation. The time-invariant case type defines the data such that the distribution of variables at time t is constant for all time steps; in other words, they are constrained by the stationary assumption. However, generation at time t may depend on data from previous time steps, even if probabilities do not change. Finally, the time-variant case type allows probabilities at time t to be defined in terms of probabilities before it, and thus probabilities may vary with time. By rewriting probability and independence specifications in terms of elementary event probabilities, we demonstrate a capacity to fully define a categorical probability model. In doing this, time series data can realistically be generated according to the constraints of a fully-defined system.