Wavelet transforms based ARIMA-XGBoost hybrid method for layer actions response time prediction of cloud GIS services

Abstract

Layer actions response time is a critical indicator of cloud geographical information services (cloud GIS Services), which is of great significance to resource allocation and schedule optimization. However, since cloud GIS services are highly dynamic, uncertain, and uncontrollable, the response time of layer actions is influenced by spatiotemporal intensity and concurrent access intensity, posing significant challenges in predicting layer action response time.To predict the response time of layer actions more accurately, we analyzed the data association of cloud GIS services. Furthermore, based on the characteristics of long-term stable trends and short-term random fluctuations in layer actions response time series, a wavelet transforms-based ARIMA-XGBoost hybrid method for cloud GIS services is proposed to improve the one-step and multi-step prediction results of layer actions response time.We generate a multivariate time series feature matrix using the historical value of the layer actions response time, the predicted value of the linear component, and the historical value of the non-linear component. There is no need to meet the traditional assumption that the linear and nonlinear components of the time series are additive, which minimizes the model’s time series requirements and enhances its flexibility. The experimental results demonstrate the superiority of our approach over previous models in the prediction of layer actions response time of cloud GIS services.