Runtime Quality Prediction for Web Services via Multivariate Long Short‐Term Memory

Abstract

Online quality prediction helps to identify the web service quality degradation in the near future. While historical web service usage data are used for online prediction in preventive maintenance, the similarities in the usage data from multiple users invoking the same web service are ignored. To improve the service quality prediction accuracy, a multivariate time series model is built considering multiple user invocation processes. After analysing the cross-correlation and similarity of the historical web service quality data from different users, the time series model is estimated using the multivariate LSTM network and used to predict the quality data for the next few time series points. Experiments were conducted to compare the multivariate methods with the univariate methods. The results showed that the multivariate LSTM model outperformed the univariate models in both MAE and RMSE and achieved the best performance in most test cases, which proved the efficiency of our method.

Early Prediction of Pressure Injury with Long Short-term Memory Networks

“…Multivariate LSTM, an improved version of LSTM, can be used to solve problems with more than one characteristic parameter. (30) The first step is to transform the prediction task into supervised learning, which simplifies the problem. Figure 4 summarizes the inputs of multivariate LSTM.…”