Likelihood-based estimation of periodicities in symbolic sequences

In this work, we propose a method of estimating periodicities in symbolic sequences, allowing for arbitrary, finite, symbol sets. Different from other common approaches, that often map the symbolic sequence to a numerical representation, we here exploit a likelihood-based formulation to represent the periodic behavior of the sequence. The performance of the proposed method is illustrated on both simulated and real DNA data, showing a notable performance gain as compared to other common estimators.