Output prediction from a sequence of data
Have a look on conditional random field. Conditional random fields  are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs is their conditional nature, resulting in the relaxation of the independence assumptions. Additionally, CRFs avoid the label bias problem. It is often used for labeling or parsing of sequential data.