Integrating EMD with Multivariate LSTM for Time Series QoS Prediction

Quality of Service (QoS) prediction is a hot topic in services computing, which has been extensively investigated in the past decade. Many approaches have been proposed to predict unknown QoS values of Web services according to their historical invocation records. These methods usually analyze each individual QoS data as a basic unit while ignoring the intrinsic characteristics of these time-series QoS data. In an extremely dynamic environment, how to capture the intrinsic and time-varying characteristics of QoS data from a finer-grained perspective becomes an essential issue to achieve accurate prediction. In this paper, we propose a hybrid QoS prediction approach by combining the Empirical Mode Decomposition (EMD) and the multivariate LSTM (Long Short-Term Memory) model. Our approach aims to capture the potential information in the historical sequence and perform accurate QoS forecasting. Experiments conducted on two realworld datasets show that our approach outperforms several state-of-the-art methods in QoS prediction performance.