A novel shape based batching and prediction approach for sunspot data using HMMs and ANNs

This paper introduces a novel approach which uses a Hidden Markov Model (HMM) based Artificial Neural Networks (ANN) 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 an ANN. The weights and Learning Rate of the ANN 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 ANNs of each batch to predict the output of the system. The Standard application of the Sun-Spot Data (SSD) was used for testing the competence of this method.