A laboratory-scale swirl-stabilized combustor is experimentally characterized for various configurations involving variable air flow rates and different fuel injection locations. Unsteady pressure and heat release rate measurements were obtained simultaneously in order to determine the stability map of the combustor for the experimented configurations. It is observed that a sharp rise in pressure amplitude coincides with a break in the dominant spectral content variation with the inlet Reynolds number. The time series data were analyzed by using the tools of symbolic dynamic filtering and the divergences among the outputs of each sub-class of observations were obtained as anomaly measures. In the proposed method, symbol strings are generated by partitioning the (finite-length) time series to construct a special class of probabilistic finite state automata (PFSA) that have a deterministic algebraic structure. The anomaly measures are defined based on the probabilistic state vectors distribution across each sub class. The method which is based on representing a given time series data as a set of PFSA is observed to be capable of predicting an impending combustion instability as well as to distinguish between the symbol-state distribution among various instability conditions. The measure also successfully captures changes in the thermoacoustic regime as a function of the fuel injection location.