TLDR: The first in a planned series of three or more papers, which constitute the first major in-road in the compositional learning programme, and a substantial step towards bridging agent foundations theory with practical algorithms. Official Abstract: We propose novel algorithms for sequence prediction based on ideas from stringology. These algorithms are time and space efficient and satisfy mistake bounds related to particular stringological complexity measures of the sequence. In this work (the first in a series) we focus on two such measures: (i) the size of the smallest straight-line program that produces the sequence, and (ii) the number of states in the minimal automaton that can compute any symbol in the sequence when given its position in base as input. These measures are interes