The Application Of Random Time Series Model In Requirement Forecasting Of Logistics

This thesis regards studying the requirement forecasting method of the logistics as the thread and uses the method of the random time series to carry on the requirement forecasting of logistics, divided into five chapters altogether.1. The outline of Logistics. The evolution of Logistics definition in China and U.S. A is analyzed, the definitions of Logistics Management and Logistics Management System are narrated.2. The approaches of the requirement forecasting of logistics. The author sets forth the definition of the Requirement of Logistics and the requirement forecasting of logistics and the function of requirement forecasting in Logistics management, explains the steps of Logistics Requirement Forecasting , enumerate the useful approaches to Logistics Manager, including Del's Philippines law, business personnel assess to determine the nature method and time array (level and smooth technology), analyzed technology and classical time array and so on, and proved with the instance.3. The forecasting approach for the stationary random time series is researched . The way of judging the stationary property of the random time series is presented ; Three kinds model of linear random are introduced; The approaches of recognition ,determining order, parameter estimation and examination of theses models are presented.4. An application of Logistics Requirement Forecasting example is given in light of the random time series mode in detail. The fourth part Take random time array linear model carry on logistics requirement forecasting, spend instance from data judgement of stationarity, melt steadily, modeling, by the discernment , making the steps , parameter to estimate and examined of the model, get predict and error and calculation of confidential interval, detailed explanation random time array linear model how about use in logistics requirement forecasting.5. The evaluating of the approach. Appraise the advantage and deficiency of the method of random time series ,and the questions that should be paid attention to while predicting in the real work.