本文關(guān)鍵詞：基于時(shí)間序列算法的網(wǎng)銀交易量預(yù)測(cè)　出處：《東華大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

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【摘要】：在“數(shù)據(jù)爆炸”的大數(shù)據(jù)時(shí)代,巨大的數(shù)據(jù)量,價(jià)值密度低的數(shù)據(jù)特點(diǎn)促使人們學(xué)習(xí)從龐大的數(shù)據(jù)轉(zhuǎn)換成有用的信息和知識(shí)的技能。數(shù)據(jù)挖掘是大數(shù)據(jù)時(shí)代的產(chǎn)物,它是從龐大數(shù)據(jù)中發(fā)現(xiàn)潛在的有用信息的過(guò)程。時(shí)間序列分析是大數(shù)據(jù)分析中很常見(jiàn)的一個(gè)部分,時(shí)間序列預(yù)測(cè)又占據(jù)了十分重要的地位,它是指根據(jù)歷史數(shù)據(jù),利用科學(xué)的方法和技術(shù)進(jìn)行合理的分析,發(fā)現(xiàn)其中的規(guī)律,最終實(shí)現(xiàn)對(duì)事物發(fā)展趨勢(shì)的評(píng)估。時(shí)間序列分析的挖掘與預(yù)測(cè)具有非常重要的現(xiàn)實(shí)意義,被廣泛運(yùn)用在宏觀經(jīng)濟(jì)、企業(yè)管理等方方面面,特別是對(duì)于金融行業(yè)的發(fā)展和金融規(guī)律的研究。本文實(shí)踐基礎(chǔ)是基于浦發(fā)銀行網(wǎng)銀交易量預(yù)測(cè)項(xiàng)目。網(wǎng)銀越來(lái)越多的進(jìn)入到人們生活中,網(wǎng)銀交易成為銀行一大主要業(yè)務(wù)。每日網(wǎng)銀交易量形成一個(gè)時(shí)間序列,所以我們?cè)噲D建立合適的模型,讓經(jīng)營(yíng)者對(duì)未來(lái)的數(shù)據(jù)進(jìn)行預(yù)測(cè)并對(duì)自己的業(yè)務(wù)作出相應(yīng)調(diào)整。本文首先分析國(guó)內(nèi)外學(xué)者對(duì)時(shí)間序列的特性、分析方法以及預(yù)測(cè)進(jìn)行的相關(guān)研究,并將其最終運(yùn)用到實(shí)際項(xiàng)目中。數(shù)據(jù)由浦發(fā)銀行提供,通過(guò)預(yù)測(cè)分析的基本方法:回歸法、移動(dòng)平均法來(lái)分析浦發(fā)銀行網(wǎng)銀交易量歷史數(shù)據(jù),從而預(yù)測(cè)2014年5月后每日網(wǎng)銀交易量。取得預(yù)測(cè)結(jié)果之后,通過(guò)預(yù)測(cè)結(jié)果即數(shù)據(jù)曲線的擬合程度來(lái)判斷該系統(tǒng)的合理性和可信性,最終搭建完成時(shí)間序列預(yù)測(cè)系統(tǒng)。本文完成工作包括:(1)分析不同時(shí)間序列模型的優(yōu)缺點(diǎn),分析時(shí)間序列、數(shù)據(jù)挖掘的發(fā)展,并闡述數(shù)據(jù)處理的一般方法進(jìn)行數(shù)據(jù)預(yù)處理,增強(qiáng)數(shù)據(jù)預(yù)測(cè)能力,優(yōu)化數(shù)據(jù)結(jié)構(gòu)和質(zhì)量,達(dá)成最終預(yù)測(cè)結(jié)果準(zhǔn)確率提升的目的。(2)分析闡述算法的用法,對(duì)時(shí)間序列經(jīng)典算法進(jìn)行綜合比較并確定合適此項(xiàng)目的時(shí)間序列預(yù)測(cè)算法。最終確定本項(xiàng)目利用Microsoft時(shí)序算法,這是使用ARIMA與ARTXP相結(jié)合的時(shí)間序列預(yù)測(cè)算法。(3)實(shí)現(xiàn)時(shí)間序列預(yù)測(cè)平臺(tái)搭建與完善,實(shí)現(xiàn)網(wǎng)銀交易量分析,改變系統(tǒng)參數(shù)配置,優(yōu)化數(shù)據(jù)處理等方式,完善系統(tǒng)的使用性。
[Abstract]:In the era of big data explosion, a huge amount of data, the data characteristics of low value density prompted the conversion from large data into useful information and knowledge learning. Data mining is the product of the era of big data, it is found that the process of potentially useful information from huge data in time series analysis. Is a very common big data analysis, time series prediction and occupies a very important position, it is based on historical data, a reasonable analysis of the use of scientific methods and technology, find the rules, the final assessment of the development trend of things. Has very important practical significance to mining and prediction of time series analysis, is widely used in macro economy, enterprise management and other aspects, especially the research for the development of Finance and financial industry. This paper is based on practice Prediction of Shanghai Pudong Development Bank online banking transaction volume of online banking projects. More and more into people's life, become a major online banking transaction banking business. The formation of a time series of daily online banking transaction volume, so we try to establish an appropriate model, so that operators for the future data to predict and make corresponding adjustments to their own business in this paper. First analyzes the domestic and foreign scholars on the time series characteristics, relevant research and analysis and forecast method, and finally applied to the practical project. The data provided by the Shanghai Pudong Development Bank, through the basic methods of predictive analysis: regression analysis of Shanghai Pudong Development bank online banking transaction amount of historical data to predict the moving average daily after May 2014 online banking transaction volume. The prediction results, the prediction results that the fitting degree of data curve to determine the system's rationality and credibility, and ultimately To build a complete time series forecasting system. This work includes: (1) analyze the advantages and disadvantages of different time series models, time series analysis, the development of data mining, and describes the general method of data processing for data preprocessing, enhanced data prediction ability, optimizing the data structure and quality, to reach a final prediction accuracy rate increase. (2) analysis algorithm usage, a comprehensive comparison of the classical time series algorithm and determine the appropriate time series prediction algorithm. This project to determine the final project using the Microsoft time series algorithm, this is the time series using ARIMA combined with ARTXP prediction algorithm. (3) time series forecasting platform to build and perfect analysis and implementation of online banking, trading volume, change the system configuration parameters, optimization of data processing, improve the use of the system.

【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13;O211.61

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 黎志勇;李寧;;基于小波的非平穩(wěn)時(shí)間序列預(yù)測(cè)方法研究[J];計(jì)算機(jī)工程與應(yīng)用;2014年10期

2 王未卿;呂亞;;基于ARIMA模型和GARCH模型的美元指數(shù)波動(dòng)性分析[J];財(cái)會(huì)月刊;2012年30期

3 馬莉;徐慶宏;;基于ARMA模型的匯率走勢(shì)預(yù)測(cè)及在商業(yè)銀行外匯理財(cái)業(yè)務(wù)中的應(yīng)用[J];西南師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年02期

4 賈澎濤;何華燦;劉麗;孫濤;;時(shí)間序列數(shù)據(jù)挖掘綜述[J];計(jì)算機(jī)應(yīng)用研究;2007年11期

5 程振源;時(shí)間序列分析:歷史回顧與未來(lái)展望[J];統(tǒng)計(jì)與決策;2002年09期

相關(guān)博士學(xué)位論文 前2條

1 段江嬌;基于模型的時(shí)間序列數(shù)據(jù)挖掘[D];復(fù)旦大學(xué);2008年

2 謝景新;非線性多步預(yù)測(cè)與優(yōu)化方法及其在水文預(yù)報(bào)中的應(yīng)用[D];大連理工大學(xué);2006年

，

本文編號(hào)：1388708