【作者】 张丽；

【导师】 傅宏；

【作者基本信息】 河南工业大学 ， 计算机应用技术， 2011， 硕士

【摘要】 粮食价格波动及其将来的走势与人们的生活息息相关，对大量的粮食交易数据深入地分析挖掘并进行价格预测，有利于管理和指导粮食交易，引导农业生产者根据具体情况选择合适的农作物，促进农业生产的信息化和市场化，降低农作物生产的成本和经济风险。因此，研究并预测农产品价格数据具有非常重要的意义。近年来，时间序列预测分析法的一个重要分支――指数平滑法，以其性能优越、适应性强和简单易用等特性，在预测方面得到迅速的发展和深入的研究，并在军事、自然科学、经济等领域得到广泛应用。随着深入研究和广泛应用，研究者发现指数平滑模型存在三个难以解决的问题，首先是平滑初值难以确定，其次是静态平滑参数难以适应时间序列本身的变化，最后是平滑参数的取值问题，大多是凭经验和多次实验，往往难以取得最佳值。本文研究重点是解决传统指数平滑模型的难点，并将其应用到粮食价格预测领域中。本文首先详细分析指数平滑法模型的理论，讨论原模型的原理和优缺点。其次，在前人取得的研究成果的基础上，通过深入分析和完整的论证，在原有二次平滑公式模型的基础上进行改进，建立基于动态参数的动态指数平滑模型，克服了预测过程中静态平滑参数难以适应时间序列本身变化的缺点，并在指数平滑算法中引入遗传算法来对参数进行优化选取，较好地解决平滑参数难以确定的问题，为决策提供更好的支持。然后，本文使用粮食交易市场的交易数据，对二次指数平滑模型、霍尔特模型和动态指数平滑模型进行对比实验，通过实验结果的对比分析，证实了动态指数平滑模型的优越性，预测的准确率较二次指数平滑模型有较显著的提高，在三种预测模型中有着较强的鲁棒性。最后，总结本文的研究成果，并对粮食价格预测的未来研究方向和重点以及在其他领域中的应用做了展望。更多还原

【Abstract】 The fluctuation of grain price and the future trends are closely related to people’s dailylives. In-depth analysis and mining large numbers of transaction data and making priceforecast are conducive to managing and directing grain trade, guiding agricultural producersto choose the right crops according to the specific conditions, promoting the informatizationand marketization of agricultural production, reducing the costs and economic risks of cropsproduction. Therefore, it’s of vital significance to study and forecast price data of agriculturalproducts. In recent years, as an important branch of time series forecasting analysis,exponential smoothing’s features of superior performance, applicability and using easily makeitself have been developed rapidly and researched in depth in the field of forecasting. And ithas been used widely in military, science, economy and so on.With in-depth research and extensive application, the researchers found that there arethree problems to solve in the exponential smoothing model. First of all, the smooth initialvalue is difficult to determine. Follows, it’s difficult for the static smoothing parameter toadapt to the change of time series. Finally, the value of smoothing parameter is mostly fromresearchers’ experience and many experiments, so it is often difficult to obtain the best values.The research emphasis in this thesis is to solve the difficulties of traditional exponentialsmoothing model, and apply it to the field of grain price forecasting.Firstly, this thesis analyzes the theory of exponential smoothing model, and discusses theadvantages and disadvantages of the original model. Secondly, on the basis of previousresearch results, through in-depth analysis and complete demonstration, it was improvedbased on original quadratic smoothing formula model. The establishment of dynamicexponential smoothing model based on dynamic parameters overcomes the shortcomings ofstatic smoothing parameter’s difficulty in adapting to the time series changes in forecastingprocess. And the introduction genetic algorithm to exponential smoothing algorithm optimizethe selection of parameters, solving the difficult problem of smoothing parameter determining,providing better support for decision-making. Then, this thesis uses truthful data of grainmarket to do contrast experiment on double exponential smoothing model, Holt model and thedynamic exponential smoothing model. The comparison of experimental results confirmsthe superiority of the dynamic exponential smoothing model and the prediction accuracy ratehas more significant improvement than the double exponential smoothing model. It hasrobustness in the three forecasting models. Finally, this thesis summarizes the results of this research, and looks forward to future research directions and priorities in grain priceforecasting and the application in other areas.更多还原