Hidden Markov models (HMMs) are particularly suitable for sequential data analysis problems. They are widely used in fields such as speech analysis, finance, word sequencing, weather forecasting, and so on.
Any source of data that produces a sequence of outputs can 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.
