多元时间序列的Web Service QoS预测方法

为准确并多步预测Web服务的服务质量（quality of service，简称QoS），方便用户选择更好的Web服务，提出了一种基于多元时间序列的QoS预测方法MulA-LMRBF （multiple step forecasting with advertisement-levenberg marquardt radial basis function）.充分考虑多个QoS属性序列之间的关联，采用平均位移法（average dimension，简称AD）确定相空间重构的嵌入维数和延迟时间，将QoS属性历史数据映射到一个动力系统中，近似恢复多个QoS属性之间的多维非线性关系.将短期服务提供商QoS广告数据加入数据集中，采用列文伯格-马夸尔特法（Levenberg-Marquardt，简称LM）算法改进的径向基（radial basis function，简称RBF）神经网络预测模型，动态更新神经网络的权重，提高预测精度，实现QoS动态多步预测.通过网络开源数据和自测数据的实验结果表明，该方法与传统方法相比有较好预测效果，更适合动态多步预测.

Web Service QoS Forecasting Approach Using Multivariate Time Series

In order to accurately forecast quality of service (QoS) of different Web services with multi-step, and help users to choose the most suitable Web service at hand, this study proposes a novel QoS forecasting approach called MulA-LMRBF (multiple-step forecasting with advertisement by levenberg-marquardt improved radial basis function network) based on multivariate time series. Considering the correlation among different QoS attributes series, phase-space reconstruction is used to map historical multivariate QoS data into a dynamic system, where the multi-dimensional nonlinear relations of QoS attributes are completely restored. Average dimension (AD) is used to estimate the embedding dimension and delay time of reconstructed phase space. The short-term QoS advertisement data of service provider is also added to form a more comprehensive data set. Then, RBF (radial basis function) neural network improved by the Levenberg-Marquardt (LM) algorithm is used to update the weight of the neural network dynamically, which improves the forecasting accuracy and realizes the dynamic multiple-step forecasting. Experiments are conducted based on several public network data sets and self-collected data set. The experimental results demonstrate that MulA-LMRBF is better than previous approaches with high precise and is more suitable for multi-step forecasting.