Building Hidden Markov Models for sequential data

The Hidden Markov Models (HMMs) are really powerful when it comes to sequential data analysis. They are used extensively in finance, speech analysis, weather forecasting, sequencing of words, and so on. We are often interested in uncovering hidden patterns that appear over time.

Any source of data that produces a sequence of outputs could produce patterns. Note that HMMs are generative models, which means that they can generate the data once they learn the underlying structure. HMMs cannot discriminate between classes in their base forms. This is in contrast to discriminative models that can learn to discriminate between classes but cannot generate data.

Getting ready

For example, let's say that we want to predict whether the weather will be sunny, chilly, or rainy tomorrow. To do this, we look at all the parameters, such as temperature, pressure, and so on, whereas the underlying state is hidden. Here, the underlying state refers to the three...