Analysis Of The Fluctuation Characteristics And Prediction Of The Turnover Volume Of Goods In China

Goods transportation maintains the normal operation of modern society.In recent years,with the development of China’s economy,the demand for modern cargo transportation has also been growing.In the document Opinions of 《the CPC Central Committee and the State Council on Accelerating the Construction of a National Unified Market issued》 in 2022,it is mentioned that for the modern circulation network,it is necessary to optimize the commercial circulation infrastructure,promote the construction of the logistics hub network,vigorously develop multimodal transport,and cultivate transport enterprises with global influence,We will promote the integrated development of transportation facilities across regions,and further promote the cost reduction and efficiency increase of logistics in the whole society.Therefore,it is of great significance to grasp the current situation of China’s freight transport industry,accurately judge the future development trend of the freight transport industry,and analyze the crisis changes in the industry,so as to scientifically formulate industrial policies and industrial upgrading,and guide the optimal allocation of freight market resources and reasonable utilization of tools.The freight transport industry has a complex system,many influencing factors and a wide range of implications,which makes it difficult to conduct quantitative analysis.In the indicators of transportation production results,the freight turnover volume of the two types of freight data,freight volume and transportation distance,is combined to reflect the regional transportation production results more comprehensively.Therefore,this paper provides the corresponding theoretical basis for the development of China’s freight transport industry through the relevant analysis and prediction of the freight turnover volume of the whole society.First of all,this paper combed a large number of documents related to fluctuation analysis.Based on the idea of factor decomposition,it decomposed the long time series of goods turnover,and found that there are four types of characteristics of goods turnover in the whole society,namely,seasonal characteristics,irregular characteristics,long-term trends and periodic fluctuations.Among the seasonal characteristics,seasonal fluctuations in February are particularly obvious,and there are two different ways of influencing irregular fluctuations and long-term trends,From the perspective of the periodicity of the turnover of goods in the whole society,with the increasingly close relationship with the world economy,China’s freight industry is increasingly affected by foreign countries.Secondly,on the basis of volatility analysis,the econometric SARIMA model and machine learning XGBoost algorithm are applied to predict the sample data.It is found that the two methods perform well in the prediction effect of the sample data,but there are still problems.Therefore,combining the advantages of the two methods,a linear weighted combination model is attempted based on the prediction of the two methods.By comparing the above three methods,It is found that the combined model is superior to SARIMA model and XGBoost algorithm in the prediction of sample data.Then the combined model is used to predict the turnover of goods in the whole society of China in the next five years.According to the prediction results,the seasonality in the next five years is still obvious,and the annual turnover of goods in 2026 will reach 28525.167 billion ton-kilometers,maintaining an overall growth trend.Finally,according to the fluctuation analysis and prediction results of the article,we summarize and propose countermeasures and suggestions as well as ideas for future research.