International crude oil price is the referential scale of spot crude oil price and refined oil price. This paper made an analysis and prediction of Brent crude oil price by ARIMA model based on its price data from November 2012 to April 2013. It indicated that model ARIMA (1,1,1) possessed good prediction effect and can be used as short-term prediction of International crude oil price.

It is meaningful and of certain theoretical value for the development of economy through analyzing fluctuation rules of international oil prices and forecasting the future trend of international oil prices. By composing the autoregressive integrated moving average (ARIMA) model and the combination model of autoregressive integrated moving average model-generalized autoregressive conditional heteroskedasticity (ARIMA-GARCH) for analyzing and forecasting international oil prices, study shows that the combination model of ARIMA (1,1,0)-GARCH (1,1) is more suitable for short-term forecasting of international oil prices with higher accuracy that the MAPE of forecasting has reduced from 1.549% to 0.045% and the RMSE of forecasting has reduced from 1.032 to 0.071.

Abstract-In order to reduce the energy consumption and enhance the robustness of wireless sensor network (WSN), this paper proposes a hierarchical clustering routing algorithm based on fuzzy mathematics (HCRAFM). To make a comprehensive analysis of WSN, it is also necessary to detect the robustness of the network. Facing the multiple random variables, the traditional robustness detection models assume that all nodes have the same weight, making it impossible to quantify the analysis indices or obtain accurate results. Thus, the fuzzy mathematics theory was introduced to the WSN robustness detection, forming a fuzzy comprehensive evaluation method. The simulation results show that the HCRAFM strikes a load balance between WSN nodes, extends the life cycle of each node, and prolongs the service life of the network. In addition, the proposed algorithm is proved to have sound robustness and strong applicability. Keywords-Routing Algorithms; Robustness Analysis; Wireless Sensor Network (WSN); Fuzzy Mathematics IntroductionIn wireless sensor network (WSN), the routing algorithm is responsible for setting up the path and mechanism of data transmission, realizing the dynamical update of network topology, and maintaining the information of network routing [1]. Therefore, the algorithm must be able to improve the energy efficiency of WSN nodes, and provide high quality services [2]. The traditional routing algorithms cannot satisfy the demand of WSN, which carries numerous different features from those of traditional networks. This calls for the design of a routing algorithm targeted at the demand of WSN [3]. The structure of a typical WSN is presented in Figure 1.

The thesis mainly estimates the change point of time series model through Bayesian method. First, through establishing the time series model, adopting conjugate prior distribution, linear regression and prior information, the parameter values related to distribution can be got. Then posterior distribution and change point can be got through computation. To reduce iterative error, Peak Algorithm is used to check the posterior distribution. Finally, the gold indexs change point of time sequence model can be got through this method.