【摘要】：隨著互聯(lián)網(wǎng)的浪潮,越來越多的互聯(lián)網(wǎng)金融公司應(yīng)運而生,互聯(lián)網(wǎng)金融風(fēng)險預(yù)測也成了互聯(lián)網(wǎng)金融公司決策時的重要一環(huán)。如螞蟻金服通過對余額寶的歷史交易額進行資金流入流出的預(yù)測;融360根據(jù)用戶的歷史還款時間序列對用戶信貸進行預(yù)測等。因此隨著互聯(lián)網(wǎng)金融交易激增,互聯(lián)網(wǎng)金融公司有必要提高系統(tǒng)風(fēng)險預(yù)測能力,將金融風(fēng)險降低到最小化。通過基于時間序列預(yù)測的方法可以為風(fēng)險預(yù)測提供參考從而降低風(fēng)險。本文一方面著眼于時間序列預(yù)測領(lǐng)域中一些模型,如基于傳統(tǒng)ARMA模型,基于神經(jīng)網(wǎng)絡(luò)模型等,并列出各模型在時間序列應(yīng)用中的優(yōu)缺點,然后在基礎(chǔ)的Elman神經(jīng)網(wǎng)絡(luò)預(yù)測模型上進行改進。另一方面著眼于時序數(shù)據(jù)的特征學(xué)習(xí),分析了時序數(shù)據(jù)特征提取與特征選擇的常用算法,并由此提出了基于時序數(shù)據(jù)的特征學(xué)習(xí)框架。通過時序數(shù)據(jù)的特征學(xué)習(xí)框架與改進的Elman神經(jīng)網(wǎng)絡(luò)預(yù)測模型相結(jié)合,提出了一個互聯(lián)網(wǎng)金融風(fēng)險預(yù)測模型。本模型著眼于實際應(yīng)用。主要內(nèi)容包含如下:1、分析了神經(jīng)網(wǎng)絡(luò)模型在時間序列預(yù)測上的應(yīng)用。包含了前向神經(jīng)網(wǎng)絡(luò)和反饋神經(jīng)網(wǎng)絡(luò)。主要針對Elman神經(jīng)網(wǎng)絡(luò)模型的研究,分析了其模型結(jié)構(gòu),各層神經(jīng)元的特點,并修改了Elman神經(jīng)網(wǎng)絡(luò)訓(xùn)練算法,在誤差計算中將歷史數(shù)據(jù)按照與當(dāng)前時間的遠近賦予相應(yīng)的權(quán)值,以及加入時序數(shù)據(jù)隨機過程,提出了改進的Elman神經(jīng)網(wǎng)絡(luò)時間序列預(yù)測模型(GT-Elman),從而增強了Elman神經(jīng)網(wǎng)絡(luò)對時間序列的預(yù)測性能。2、分析了時序數(shù)據(jù)常用的特征提取算法與特征選擇算法。通過將時域序列轉(zhuǎn)化為頻域序列如快速傅立葉變換,離散小波變換等特征提取算法,提取時序數(shù)據(jù)中的特征;在特征選擇算法里分析了Clamping Network的網(wǎng)絡(luò)結(jié)構(gòu),算法思想和缺陷,并根據(jù)該缺陷提出了一種改進的Clamping Network(DS-Clamping),從而增強了Clamping Network在特征選擇上的性能。相比于直接使用原始時序數(shù)據(jù)作為輸入,通過這種對時序數(shù)據(jù)特征學(xué)習(xí)模型得到時序數(shù)據(jù)的特征作為時間序列預(yù)測模型的輸入,能更好的提高預(yù)測精度,提高系統(tǒng)的預(yù)測性能。3、針對互聯(lián)網(wǎng)金融風(fēng)險預(yù)測系統(tǒng)的設(shè)計和開發(fā)。本系統(tǒng)采用基于SpringMVC框架,結(jié)合Bootstrap、Echart、JQuery搭建了互聯(lián)網(wǎng)金融風(fēng)險預(yù)測系統(tǒng)。并可視化展示了系統(tǒng)返回的結(jié)果,結(jié)果表明本系統(tǒng)有著較好的實用價值。
[Abstract]:With the tide of the Internet, more and more Internet financial companies emerge as the times require, and Internet financial risk prediction has become an important part of the decision-making of Internet financial companies. For example, Ant Financial Services Group forecasts the inflow and outflow of funds through the historical transaction volume of Yu'e Bao; Rong 360 forecasts the credit of users according to the historical repayment time series of users. Therefore, with the proliferation of Internet financial transactions, it is necessary for Internet financial companies to improve their ability to predict systemic risks and minimize financial risks. The method based on time series prediction can provide reference for risk prediction and reduce risk. On the one hand, this paper focuses on some models in the field of time series prediction, such as the traditional ARMA model and neural network model, and lists the advantages and disadvantages of each model in the application of time series. Then the prediction model based on Elman neural network is improved. On the other hand, based on the feature learning of temporal data, the common algorithms of feature extraction and feature selection for temporal data are analyzed, and a framework of feature learning based on temporal data is proposed. By combining the feature learning framework of time series data with the improved Elman neural network prediction model, an Internet financial risk forecasting model is proposed. This model focuses on practical application. The main contents are as follows: 1. The application of neural network model in time series prediction is analyzed. It includes forward neural network and feedback neural network. Aiming at the research of Elman neural network model, the structure of the model and the characteristics of each layer of neurons are analyzed, and the training algorithm of Elman neural network is modified. In the error calculation, the historical data is assigned the corresponding weight value according to the distance and near to the current time. An improved Elman neural network time series prediction model (GT-Elman) is proposed by adding time series random process, which enhances the performance of Elman neural network in time series prediction. The feature extraction algorithm and feature selection algorithm are analyzed. By transforming the time-domain sequence into frequency-domain sequences such as fast Fourier transform (FFT), discrete wavelet transform (DWT) and other feature extraction algorithms, the features of time-series data are extracted. In the feature selection algorithm, the network structure, algorithm idea and defect of Clamping Network are analyzed, and an improved Clamping Network (DS-Clamping is proposed to improve the performance of Clamping Network in feature selection. Compared with the direct use of the original time series data as the input, the feature of the time series data can be obtained by using the feature learning model of the time series data as the input of the time series prediction model, which can improve the prediction accuracy better. Improve the prediction performance of the system. 3, for the Internet financial risk forecasting system design and development. This system uses SpringMVC framework and Bootstrap,Echart,JQuery to build Internet financial risk forecasting system. The results show that the system has good practical value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F724.6;F832;TP183

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