Abstract: A method of encoding sequential data that allows encoding a subsequence of full sequences as a composite data symbol, wherein a subsequence is comprised of a maximum of one original data element, and a maximum of K original data elements. These composite data symbols, arranged sequentially, can then be used to train a machine learning model, and thus reduce complexity when a strict ordering within the context of the original data subsequences is not required, while still modeling synergies between the sequential data elements. Further, the method determines a set of related data elements to a composite symbol at the next time step, given the original subsequence.