Tourism Traffic Demand Prediction Using Google Trends Based on EEMD-DBN

Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.

AN ANALYTIC HIERARCHY PROCESS MODEL OF GROUP CONSENSUS

In group decision making, a certain degree of consensus is necessary to derive a meaningful and valid outcome. This paper proposes a consensus reaching model for a group by using the Analytic Hierarchy Process （AHP）. It supports people to improve their group consensus level through an updating of their judgments. In this model, a moderator suggests the most discoraant aeclslon manet to update his judgment in each step. The proposed consensus reaching model allows decision makers to accept or reject the suggestion from the moderator. This model ensures that the judgment updating is effective and the final solution will be of acceptable consistency. Finally, a numerical example is given to illustrate the validity of the proposed consensus reaching model.

BILATERAL RELATIONS BETWEEN CHINA AND THE UNITED STATES:POLICY PRIORITIZATION WITH THE ANP

Improving relations between the People＇s Republic of China （PRC） and the United States of America （US） and ensuring that they work together as allies rather than as competitors can serve as a stabilizing force against armed conflict, particularly with surrounding nations. The economic, social, and political relationships between the PRC and US have progressed along a hilly journey. As the second largest economy in the world, the PRC has continued to develop its military and is determined to climb the technological ladder. This growth has led the US and the PRC to be referred to as a G-2 of superpowers. As the US hegemony continues to weaken this G-2 relationship is becoming more important. With significant economic, political, and security issues at stake it is crucial that efforts to strengthen these relations are prioritized and implemented. A rigorous prioritization process, the Analytic Network Process （ANP） is used herein to prioritize the efforts and initiatives in the G-2 relationship. The model is presented with results and the extensive sensitivity analysis present additional insight into the suggested solutions.