Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76662
Title: A web service QoS forecasting approach based on multivariate time series
Authors: Zhang, P
Wang, L
Li, W
Leung, H 
Song, W
Keywords: Dynamic multiple step forecasting
LM algorithm
Multivariate time series
Phase-space reconstruction
Quality of Service
RBF neural network
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: 24th IEEE International Conference on Web Services, ICWS 2017, Honolulu, United States, 25 - 30 June 2017, 8029756, p. 146-153 How to cite?
Abstract: In order to accurately forecast Quality of Service (QoS) of different Web Services, this paper proposes a novel QoS forecasting approach called MulA-LMRBF (Multi-step fore-casting with Advertisement and Levenberg-Marquardt improved Radial Basis Function) based on multivariate time series. Considering the correlation among different QoS attributes, we use phase-space reconstruction to map historical multivariate QoS data into a dynamic system, use Average Dimension (AD) to estimate the embedding dimension and delay time of reconstructed phase space. We also add the short-term QoS advertisement data of service provider 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. The experimental results demonstrate that MulA-LMRBF is better than previous approaches in term of precision and is more suitable for multi-step forecasting.
URI: http://hdl.handle.net/10397/76662
ISBN: 9781538607527
DOI: 10.1109/ICWS.2017.27
Appears in Collections:Conference Paper

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