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Broadly used and applied in many domains, Hidden Markov Models are a well established formalism, both in computer science and statistics. Among other reasons, they owe their popularity to a fast fitting method, i.e., the Baum-Welch algorithm, allowing to adjust models to a variety of input data. Using expectation and maximization phases, BW assures an increase to the model likelihood at every iteration. Yet, to initialize the sequence of expectationmaximization (EM) steps, it is a standard procedure to start the BW algorithm from randomly generated values. We propose a, simple and fast, deterministic pre-fitting approach which derives the BW’s initial values directly from the input data.

Piecewise Aggregation for HMM fitting. A pre-fitting model for seamless integration with time series data