Forecasting with the Baum-Welch Algorithm and Hidden Markov Models

Leonard Baum and Lloyd Welch designed a probabilistic modelling algorithm to detect patterns in Hidden Markov Processes. They built upon the theory of probabilistic functions of a Markov Chain and the Expectation–Maximization (EM) Algorithm - an iterative method for finding maximum likelihood or maximum a-posteriori estimates of parameters in statistical models, where the model depends on unobserved latent variables. The Baum–Welch Algorithm initially proved to be a remarkable code-breaking and speech recognition tool but also has applications for business, finance, sciences and others. The algorithm finds unknown parameters of a Hidden Markov Model: the maximum likelihood estimate of the parameters of a Hidden Markov Model given a set of observed feature vectors. Two step process: 1. computing a-posteriori probabilities for a given model; and 2. re-estimation of the model parameters.