A novel shape based batching and prediction approach for time series using HMMs and FISs

This paper introduces a novel approach which uses a Hidden Markov Model (HMM) based Fuzzy Inference System (FIS) for prediction of systems that are non deterministic, dynamical and chaotic in nature. The HMM is used for shape based batch creation of training data which is then processed one batch at a time by a FIS. The Membership functions and Rule Base of the FIS are tweaked to predict the correct output for an input dataset. The novel Prediction method used here exploits the Pattern Identification prowess of the HMM for batch selection and the FIS of each batch to predict the output of the system. The Benchmark applications of the Mackey Glass Time Series (MGTS) as well as the Sunspot Data time-series were used for testing the competence of this method.