Symbolic pattern recognition for sequential data

摘要

Sources of sequential data surround and pervade our lives. Our bodies continuously generate sequential data such as heart rate and blood pressure. In global finance, stock indices and currency exchange rates change every second. The movement of clouds, the coordinates of the planets, the score of a soccer game, etc., are all examples of sequential data transitioning fromone state to another in regular time steps. There a mature body of literature related to modeling time-dependent sequential data, or time series, in which every model relies upon specific assumptions for applicability, to data. However, there other kinds of sequential data that are not collected with respect to time., for example DNA sequences (if vve ignore the gene mutations). Modeling of this type of sequential data does not have a body of literature that is as mature. This is somewhat perplexing, since sequential data modelingshould cover both time-dependent and non-time-dependent data.