The Research Of Forecast Method And Application Of Cargo

Cargo is an important indictor for determining the logistic needs of the region. It is also the main basis for determining the scale of the logistic infrastructure and making industrial policies. The reasonable and reliable results of the Cargo Forecast will have a direct impact on the invent income of region's logistic infrastructure and the development of related logistics enterprises. It is also of significant meanings in the distribution of resources in the region and making strategies for the logistics development.There are many predictive methods now. Besides traditional and classic predictive methods, such as time series smoothing method, trend extrapolation method, regression method, etc, there are many new intelligent algorithms to be applied on forecasting, such as neural network prediction, ant algorithm, etc. In the thesis, we know that the complexity and progressiveness of algorithm would not improve the forecast accuracy. Because of the localization of the predictive method and the confine of the predictive person's knowledge, the predictive result is not satisfied in the practice. Therefore, only after analysis the forecast target and according the application range of the algorithm can we make the result reliable. In this case, how to choose a reasonable method to predict Cargo will be very importance.There are six parts in this thesis. The first part introduces the backgrounds, aim, significance, main content and methods used in the research, and literatures related. Then, the second part outlines the basic theories related to forecast and focus on the predictive definition, predictive principle predictive classification and predictive steps. And next, the third part analyzes the impact of the development of freight-related factors, and focuses on cargo forecasts can be applied to the method, based on this analysis of the ways of the advantages and disadvantages, as well as for the back of empirical research provides a theoretical Basis. The fourth part is a case analysis, combined with the previous analysis, the author selects the moving average model, the exponential smoothing model, the index curve model, the regression model, the gray model and the combination forecast model to predict the Cargo of Shandong Province. Through comparing the results, the author finds the combination forecast model is a reliable model. Finally, the author draws a conclusion and shows the further research.