Early Prediction of Temporal Sequences Based on Information Transfer

Ning Yang, Jian Peng, Yu Chen, Changjie Tang

Abstract: In recent years, early prediction for ongoing sequences has been more and more valuable in a large variety of time-critical applications which demand to classify an ongoing sequence in its early stage. There are two challenging issues in early prediction, i.e. why an ongoing sequence is early predictable and how to reasonably determine the parameter k optimal , the minimum number of elements that must be observed before an accurate classification can be made. To address these issues, this paper investigates the kinetic regularity of the information transfer in sequence data set. As a result, a new concept of Accumulatively Transferred Information (ATI) and its kinetic model in early predictable sequences are proposed. This model shows that the information transfer in early predictable sequences follows Inverse Heavy-tail Distribution(IHD), and the most uncertainty of an early predictable sequence is eliminated by only few of its preceding elements, which is exactly the intrinsic and theoretically sound ground of the feasibility of early prediction. Based on the kinetic model, a heuristic algorithm is proposed to learn the parameter k optimal . The experiments are conducted on real data sets and the results validate the reasonableness and effectiveness of the proposed theory and algorithm.