【摘要】：水果是我國第四大作物類別。2013年,我國水果產(chǎn)量為25093萬噸,實現(xiàn)總產(chǎn)值6969億元。而我國人均鮮果消費僅為37.8kg,遠(yuǎn)低于發(fā)達(dá)國家水平,水果消費市場潛力巨大。但水果產(chǎn)業(yè)的發(fā)展也面臨諸多挑戰(zhàn),比較典型的就是水果市場價格波動。這種波動直接影響了水果生產(chǎn)經(jīng)營者的積極性,更是導(dǎo)致水果經(jīng)營企業(yè)面臨較大的經(jīng)營風(fēng)險。開展水果價格預(yù)測和波動預(yù)警對水果企業(yè)而言具有重要意義。因此本文以水果價格為研究對象,主要研究內(nèi)容和結(jié)論如下:第一,開展水果價格預(yù)測。本文通過BP神經(jīng)網(wǎng)絡(luò)、SVM和ARMA模型對水果價格進(jìn)行年度和月度預(yù)測。年度預(yù)測中,通過供給和需求指標(biāo)構(gòu)建了預(yù)測模型,SVM預(yù)測誤差在5%和10%以內(nèi)的分布優(yōu)于BP神經(jīng)網(wǎng)絡(luò);月度預(yù)測中,通過水果價格時間序列構(gòu)建了預(yù)測模型,BP神經(jīng)網(wǎng)絡(luò)和ARMA對三種水果價格的預(yù)測誤差基本在5%以內(nèi),而SVM均在1%以內(nèi)�；陬A(yù)測誤差比較,最終確定SVM作為預(yù)測預(yù)警模型。第二,開展水果價格波動預(yù)警�；陬A(yù)測分析,本文最終采用SVM對水果價格波動進(jìn)行預(yù)警。本文以水果價格波動為警情指標(biāo),以均值和標(biāo)準(zhǔn)差的統(tǒng)計方法確定警度和警限,在水果價格預(yù)測基礎(chǔ)上,通過SVM實現(xiàn)了對水果價格年度和月度波動預(yù)警。其中SVM對三種水果的年度預(yù)警準(zhǔn)確率為64.70%,100%和94.12%,月度預(yù)警準(zhǔn)確率為100%、91.67%和83.33%。第三,分析水果企業(yè)在水果價格波動背景下如何進(jìn)行價格波動預(yù)警應(yīng)對�；谒麅r格預(yù)測和波動預(yù)警分析,本文認(rèn)為水果批發(fā)企業(yè)應(yīng)該強(qiáng)化自身的信息化建設(shè),針對不同的警情和警度完善水果價格波動的預(yù)警預(yù)案,根據(jù)水果價格波動特征優(yōu)化企業(yè)的價格策略,以此增強(qiáng)價格波動的風(fēng)險應(yīng)對能力。本文主要有以下創(chuàng)新點:(1)將計量分析應(yīng)用到企業(yè)所處的宏觀產(chǎn)業(yè)環(huán)境的分析中,增強(qiáng)了管理措施的針對性和有效性。(2)改進(jìn)BP神經(jīng)網(wǎng)絡(luò),通過多次運算取最優(yōu)均值,提高了模型的穩(wěn)定性和預(yù)測精度;(3)改進(jìn)模型選取方法,將BP神經(jīng)網(wǎng)絡(luò)、SVM和ARMA模型對比選擇,優(yōu)化了預(yù)測預(yù)警模型的選擇過程;(4)改進(jìn)預(yù)警警限和警度的設(shè)置方式,以正向波動和負(fù)向波動分別統(tǒng)計,通過均值、標(biāo)準(zhǔn)差和單側(cè)置信區(qū)間分別設(shè)定警限和警度,增強(qiáng)了預(yù)警警限和警度的可行性和科學(xué)性。
[Abstract]:Fruit is the fourth largest crop category in China. In 2013, China's fruit output was 250.93 million tons, achieving a total output value of 696.9 billion yuan. However, the per capita consumption of fresh fruit in China is only 37.8 kg, which is much lower than that in developed countries, and the potential of fruit consumption market is huge. However, the development of fruit industry is also facing many challenges, more typical is the price fluctuation of fruit market. This fluctuation directly affects the enthusiasm of fruit producers and operators, but also leads to greater business risks for fruit enterprises. It is of great significance for fruit enterprises to carry out fruit price prediction and fluctuation early warning. Therefore, this paper takes fruit price as the research object, the main research contents and conclusions are as follows: first, carry out fruit price prediction. In this paper, BP neural network, SVM and ARMA models are used to predict the annual and monthly fruit prices. In the annual forecast, the prediction model is constructed by the index of supply and demand. The distribution of SVM prediction error less than 5% and 10% is better than that of BP neural network. In monthly prediction, the prediction model is constructed by fruit price time series. The prediction error of BP neural network and ARMA for three kinds of fruit prices is basically less than 5%, while SVM is less than 1%. Based on the comparison of prediction error, SVM is finally determined as the prediction and early warning model. Second, carry out early warning of fruit price fluctuation. Based on the prediction analysis, this paper finally uses SVM to warn the fruit price fluctuation. In this paper, the fluctuation of fruit price is taken as the warning index, and the alarm degree and warning limit are determined by the statistical method of mean and standard deviation. On the basis of fruit price prediction, the early warning of annual and monthly fluctuation of fruit price is realized by SVM. The annual early warning accuracy of SVM for three kinds of fruits was 64.70%, 100% and 94.12%, and the monthly early warning accuracy was 100%, 91.67% and 83.33% respectively. Third, analyze how to deal with the price fluctuation of fruit enterprises under the background of fruit price fluctuation. Based on the prediction of fruit price and the early warning analysis of fluctuation, this paper holds that fruit wholesale enterprises should strengthen their own information construction and perfect the early warning plan of fruit price fluctuation according to different warning conditions and degrees of warning. According to the characteristics of fruit price fluctuation, the price strategy of enterprises is optimized to enhance the risk response ability of price fluctuation. The main innovations of this paper are as follows: (1) the econometric analysis is applied to the analysis of the macro industrial environment in which the enterprise is located, which enhances the pertinence and effectiveness of the management measures. (2) the BP neural network is improved and the optimal mean value is obtained by multiple operations. The stability and prediction accuracy of the model are improved. (3) the selection method of the model is improved, and the selection process of the prediction and early warning model is optimized by comparing the BP neural network, SVM and ARMA models. (4) the setting mode of early warning limit and alarm degree is improved, and the feasibility and science of early warning limit and alarm degree are enhanced by setting alarm limit and alarm degree by means of mean value, standard deviation and unilateral confidence interval, respectively.
【學(xué)位授予單位】：華中農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F323.7

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