The Research Of Shipping Company Export Container Data Visualization And Prediction Model

Since the global economic crisis has broken out in 2008,the international trade shows continuous slow growth.As a benchmark of international trade,the shipping business has been shocked greatly caused by weak economic growth.The huge losses or bankruptcy-reorganization of shipping enterprises are common.On the one hand the global economic crisis has led to that shipping enterprises are facing severe pressure to survive,on the other hand the traditional philosophy and way of thinking of shipping enterprises can not adapt to the current competitive market environment.Therefore,enterprises must change thinking.By using advanced technology to build a modern shipping enterprise,it is not only their own development requirements,but also comply with national strategy of building Ocean Power and Maritime Power.Under this background,with the help of Big Data prediction and visualization technologies,based on the export container data of Z shipping company shanghai branch from 2010 to 2016,this thesis has predicted the trend of data by using ARIMA model in time series analysis field,has made a dynamic data map which shows the global distribution of export container by using R-project data visualization and public vector geographic datasets,and has explored the factors that affected the amount of export container by using multiple regression model.Based on these research,this thesis combined mature mathematical prediction models and advanced data visualization tools,used dynamic data map to show statistical regularity which implied in complex temporal-spatial data,and provided further direction for making business decision via multiple regression.This set of methods provides a new perspective for enterprises to rediscover the value of data,improve business model,and enhance the Soft Power of competitiveness.