Enhancing Multivariate Time Series Classification Using LSTM and Evidence Feed Forward HMM

This paper presents a hybrid classifier that combines a Long Short Term Memory (LSTM) and an Evidence Feed Forward Hidden Markov Model (EFF-HMM) to classify multivariate time series (MTS). Learning of the EFF-HMM is performed based on mistakes of the LSTM. Confusion matrix obtained after classification of the MTS by the LSTM is employed during a learning process of the EFF-HMM. The EFF-HMM efficiently models a temporal characteristic and uncertainty of the MTS. The hybrid classifier combines strengths of the LSTM and EFFHMM to enhance the accuracy of the MTS classification. The proposed method is tested on Human Activity Recognition (HAR) dataset to classify various human activities. The experiments and results show the proposed method outperforms as compared to state of the art methods.