Chaotic time series prediction using combination of Hidden Markov Model and Neural Nets

This paper introduces a novel method for the prediction of chaotic time series using a combination of Hidden Markov Model (HMM) and Neural Network (NN). In this paper, an algorithm is proposed wherein an HMM, which is a doubly embedded stochastic process, is used for the shape based clustering of data. These data clusters are trained individually with Neural Network. The novel prediction approach used here exploits the Pattern Identification prowess of the HMM for cluster selection and uses the NN associated with each cluster to predict the output of the system. The effectiveness of the method is evaluated by using the benchmark chaotic time series: Mackey Glass Time Series (MGTS). Simulation results show that the given method provides a better prediction performance in comparison to previous methods.