The Application Of Forecasting Methods In Grain Trade

The grain trade is a special trade in economy,and its production and management is in accord with economic rules. The requirement how to estimate the future trend of some economic indexes in order to provied assistant decision for grain management department become imperious.The existing forecasting tools are not fit for generic users by reason of their high price and speciality.On the other hand,some special forecasting software just aim at the given domain or the appointed project,and most of them are integrated into MIS or ERP systems,which strongly depend on the running environment.Accordingly,the requirement to produce a forecasting system for grain trade whose users are decisionmakers in grain management department is brought forward.After studied forecasting models and methods,we make use of economic forecasting technology in "Intelligent DSS of Grain"(IDSSG),and combined the advantage and specialities of data warehouse with forecasting technology to realized the grain forecasting system based on data warehouse.This paper studies and compares several kinds of frequently used forecasting models:Exponential Smoothing Model,Season Adjustment Model,Linear Random Model,Linear Regression Model,Artificial Neural Network Model.In these models,Exponential Smoothing Model,Season Adjustment Model and Linear Random Model are used in time series analysis,while Linear Regression Model is fit for cause and effect analysis,and Neural Network Model is able to both time series analyze and cause and effect analyze.With the target of understandability and practicability,we apply those forecasting models mentioned in grain trade and realized a Grain Forcasting and Decision System(GFDS) for common program managers,which take the IDSSG as background.Based on data warehouse of grain,the GFDS integrates database,model base and method base into one system.The data warehouse stores and synthesizes the forecast topic and information.The model base and method base provide quantitative analysis(model computation) and assistant decision information.The database include basic data,time series data and pertinent data dictionary;The content of model base is forecasting instance dictionary,model parameter interface tables,model storage tables and forecasting result storage tables;The method base is made up of forecasting method dictionary and algorithm class base.The integrated forecast environment is Designed and realized,which supports customizing forcast instance.There are several kinds of forcast method to choose and users will get understandable results.After compare the RMS(Rooted Means of Square Errors) and the curve's nicety,it will be decided which model will be chosen. The whole process including forecast instance definition,forecast method choosing,model creating,model proving,model storage and results analysing of multi-scenarios is realized.The system supplies many functions such as data management,forecast application,data query,operation guidance and error disposal.The friendly graph user interface directs users to work and feed the results back in a understandable way.It's no need for users to know much about the forecast technology to control and adjust the system easily,which allows of forecast the complex and synthetical science to serv for common users.So the system is much utilizable.The data management module comprises three sub-functions which are data produce,data operation and data maintenance.It is used to produce,input,audit and maintain the data dictionaries and time series data,so it is the base of forecast model building and forecast running.The data query module is able to query data dictionaries,data information and forecast models in order to make users know clearly about all kinds of data and model,and then user can analys the data and give some judgement and estimation.The operation guidance and error disposal module catchs the errors and abnomities occured during the process of operation and prompts the user by dialog and text in a friendly way.Meanwhile,some advice about how to dea...