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  本文選題：預(yù)測 + 時間序列　； 參考：《北京工業(yè)大學(xué)》2015年碩士論文

【摘要】：隨著企業(yè)信息化和數(shù)字化的不斷深入,對于數(shù)據(jù)的分析要求也越來越高。企業(yè)對于財務(wù)數(shù)據(jù)已經(jīng)不再滿足于計算準確的要求。隨著企業(yè)清算周期的不斷縮短,關(guān)注點現(xiàn)在已經(jīng)逐漸放在了財務(wù)數(shù)據(jù)的計算及時性上。針對企業(yè)的這一需求,對企業(yè)的貨運收入數(shù)據(jù)預(yù)測方法進行了分析及研究。由于實際生產(chǎn)過程中出現(xiàn)的數(shù)據(jù)分布規(guī)律并不統(tǒng)一,因此采用不同的方法分別進行分析處理。此課題主要做了三個工作。第一,針對可以呈線性分布的貨運收入數(shù)據(jù),使用基于時間序列的方法進行預(yù)測。在這個工作中,除了論述當前貨運收入預(yù)測中經(jīng)常使用的移動平均法之外,還詳細論述了使用指數(shù)平滑法和一次線性回歸法的預(yù)測過程,并且詳細論述了這些方法中使用的重要參數(shù)的取值原因。第二,對于呈曲線分布的貨運收入數(shù)據(jù),使用多元函數(shù)回歸法、BP神經(jīng)網(wǎng)絡(luò)法進行預(yù)測。本文在這個工作的論述中,除了給出通過計算得到的預(yù)測值,還對預(yù)測值的誤差進行了分析,解釋了文中BP神經(jīng)網(wǎng)絡(luò)各層神經(jīng)元個數(shù)的取值原因。第三,結(jié)合前面兩個部分的內(nèi)容,使用BP神經(jīng)網(wǎng)絡(luò)與時間序列結(jié)合的預(yù)測方法,對貨運收入數(shù)據(jù)進行預(yù)測。在這個工作中,除了論述了這種預(yù)測方法之外,還指出了這種預(yù)測方法同前面預(yù)測方法的不同之處,分析了這種方法相比前面兩種方法的優(yōu)勢。通過這篇論文的論述,實現(xiàn)了對于大多數(shù)情況下公司貨運收入數(shù)據(jù)的預(yù)測方法。對于自主開發(fā)的軟件系統(tǒng)來說,使用商業(yè)軟件工具進行的預(yù)測,無法直接移植到系統(tǒng)中。這篇論文針對不同的貨運收入數(shù)據(jù)的實際案例,詳細分析了預(yù)測的過程。對于每種預(yù)測技術(shù),詳細論述了具體的算法。通過這篇論文的論述,可以將這些技術(shù)在系統(tǒng)中得到實現(xiàn)。對于公司來說,可以通過收入預(yù)測對運營情況做出調(diào)整,對于公司的管理有著十分重要的意義。
[Abstract]:With the deepening of enterprise informatization and digitization, the requirement of data analysis is becoming higher and higher. Enterprises are no longer satisfied with the requirements of accurate calculation for financial data. As the liquidation cycle continues to shorten, the focus is now on the timeliness of financial data calculation. According to the demand of enterprises, this paper analyzes and studies the forecasting method of freight revenue data. Because the data distribution law in the actual production process is not uniform, different methods are used to analyze and process the data. This subject has done three main work. Firstly, the time series method is used to predict the freight revenue data which can be linearly distributed. In this work, in addition to discussing the moving average method, which is often used in the current freight revenue forecasting, the prediction process using exponential smoothing method and primary linear regression method is also discussed in detail. The reasons for the important parameters used in these methods are discussed in detail. Secondly, for the freight revenue data with curve distribution, the multivariate function regression method and BP neural network method are used to forecast the data. In this paper, in addition to the calculated prediction value, the error of the prediction value is analyzed, and the reason of the number of neurons in each layer of BP neural network is explained. Thirdly, combining the content of the former two parts, using BP neural network and time series forecasting method, the freight revenue data are forecasted. In this work, in addition to the discussion of this forecasting method, it also points out the difference between this prediction method and the previous prediction method, and analyzes the advantages of this method compared with the former two methods. Through the discussion of this paper, the forecasting method of the company freight revenue data is realized in most cases. For the self-developed software system, the prediction using commercial software tools can not be directly transplanted to the system. This paper analyzes the forecasting process in detail according to the actual cases of different freight revenue data. For each prediction technique, the specific algorithm is discussed in detail. These technologies can be realized in the system through the discussion of this paper. For the company, it is very important for the management of the company to adjust the operating situation through the revenue forecast.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F259.2;TP183

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本文編號：2060491