A method and system to generate vectors that represent linearly progressing entities like time are disclosed. Traditional methods of vectorisation account for semantic or associative similarity of the entities. Thus, vectors conveying semantic information do not convey structural relations between such entities. The method allows for the representation of such structural information, for example the months in a year. The vectors generated by the invention encode this relation between the months such that one can interpret the sequence of the months, the difference between then and their cyclic nature. The method works in a manner similar to a genetic code, where subsequent “child” vectors are generated by related “parents”, thus encoding the similarity and the distance of the sequential entities. An object of the inventions to allow algorithms in machine learning to easily learn over temporal entities its natural text.