Data stream prediction using incremental hidden markov models

Abstract

In this paper, we propose a new technique for time-series prediction. Here we assume that time-series data occur depending on event which is unobserved directly, and we estimate future data as output from the most likely event which will happen at the time. In this investigation we model time-series based on event sequence by using Hidden Markov Model(HMM), and extract time-series patterns as trained HMM parameters. However, we can't apply HMM approach to data stream prediction in a straightforward manner. This is because Baum-Welch algorithm, which is traditional unsupervised HMM training algorithm, requires many stored historical data and scan it many times. Here we apply incremental Baum-Welch algorithm which is an on-line HMM training method, and estimate HMM parameters dynamically to adapt new time-series patterns. And we show some experimental results to see the validity of our method. © 2009 Springer Berlin Heidelberg.