本文關鍵詞：基于時間序列算法的網(wǎng)銀交易量預測　出處：《東華大學》2016年碩士論文　論文類型：學位論文

  更多相關文章： 時間序列 預測算法 數(shù)據(jù)挖掘

【摘要】：在“數(shù)據(jù)爆炸”的大數(shù)據(jù)時代,巨大的數(shù)據(jù)量,價值密度低的數(shù)據(jù)特點促使人們學習從龐大的數(shù)據(jù)轉(zhuǎn)換成有用的信息和知識的技能。數(shù)據(jù)挖掘是大數(shù)據(jù)時代的產(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程。時間序列分析是大數(shù)據(jù)分析中很常見的一個部分,時間序列預測又占據(jù)了十分重要的地位,它是指根據(jù)歷史數(shù)據(jù),利用科學的方法和技術進行合理的分析,發(fā)現(xiàn)其中的規(guī)律,最終實現(xiàn)對事物發(fā)展趨勢的評估。時間序列分析的挖掘與預測具有非常重要的現(xiàn)實意義,被廣泛運用在宏觀經(jīng)濟、企業(yè)管理等方方面面,特別是對于金融行業(yè)的發(fā)展和金融規(guī)律的研究。本文實踐基礎是基于浦發(fā)銀行網(wǎng)銀交易量預測項目。網(wǎng)銀越來越多的進入到人們生活中,網(wǎng)銀交易成為銀行一大主要業(yè)務。每日網(wǎng)銀交易量形成一個時間序列,所以我們試圖建立合適的模型,讓經(jīng)營者對未來的數(shù)據(jù)進行預測并對自己的業(yè)務作出相應調(diào)整。本文首先分析國內(nèi)外學者對時間序列的特性、分析方法以及預測進行的相關研究,并將其最終運用到實際項目中。數(shù)據(jù)由浦發(fā)銀行提供,通過預測分析的基本方法:回歸法、移動平均法來分析浦發(fā)銀行網(wǎng)銀交易量歷史數(shù)據(jù),從而預測2014年5月后每日網(wǎng)銀交易量。取得預測結(jié)果之后,通過預測結(jié)果即數(shù)據(jù)曲線的擬合程度來判斷該系統(tǒng)的合理性和可信性,最終搭建完成時間序列預測系統(tǒng)。本文完成工作包括:(1)分析不同時間序列模型的優(yōu)缺點,分析時間序列、數(shù)據(jù)挖掘的發(fā)展,并闡述數(shù)據(jù)處理的一般方法進行數(shù)據(jù)預處理,增強數(shù)據(jù)預測能力,優(yōu)化數(shù)據(jù)結(jié)構(gòu)和質(zhì)量,達成最終預測結(jié)果準確率提升的目的。(2)分析闡述算法的用法,對時間序列經(jīng)典算法進行綜合比較并確定合適此項目的時間序列預測算法。最終確定本項目利用Microsoft時序算法,這是使用ARIMA與ARTXP相結(jié)合的時間序列預測算法。(3)實現(xiàn)時間序列預測平臺搭建與完善,實現(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.

【學位授予單位】：東華大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP311.13;O211.61

【參考文獻】

相關期刊論文 前5條

1 黎志勇;李寧;;基于小波的非平穩(wěn)時間序列預測方法研究[J];計算機工程與應用;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)波動性分析[J];財會月刊;2012年30期

3 馬莉;徐慶宏;;基于ARMA模型的匯率走勢預測及在商業(yè)銀行外匯理財業(yè)務中的應用[J];西南師范大學學報(自然科學版);2009年02期

4 賈澎濤;何華燦;劉麗;孫濤;;時間序列數(shù)據(jù)挖掘綜述[J];計算機應用研究;2007年11期

5 程振源;時間序列分析:歷史回顧與未來展望[J];統(tǒng)計與決策;2002年09期

相關博士學位論文 前2條

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

2 謝景新;非線性多步預測與優(yōu)化方法及其在水文預報中的應用[D];大連理工大學;2006年

，

本文編號：1388708