Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm

Abstract

Intelligent systems that can predict future events can make more reliable decisions. Active LeZi, a sequential prediction algorithm, can reason about the future in stochastic domains without domain-specific knowledge. In this article, potential of constructing a prediction algorithm based on data compression techniques are investigated. Active LeZi prediction algorithm approaches sequential prediction from an information-theoretic standpoint. For any sequence of events that can be modeled as a stochastic process, ALZ uses Markov models to optimally predict the next symbol