基于金融数据的时间序列研究与应用
发布时间：2018-11-18 18:02
【摘要】：随着互联网的浪潮,越来越多的互联网金融公司应运而生,互联网金融风险预测也成了互联网金融公司决策时的重要一环。如蚂蚁金服通过对余额宝的历史交易额进行资金流入流出的预测;融360根据用户的历史还款时间序列对用户信贷进行预测等。因此随着互联网金融交易激增,互联网金融公司有必要提高系统风险预测能力,将金融风险降低到最小化。通过基于时间序列预测的方法可以为风险预测提供参考从而降低风险。本文一方面着眼于时间序列预测领域中一些模型,如基于传统ARMA模型,基于神经网络模型等,并列出各模型在时间序列应用中的优缺点,然后在基础的Elman神经网络预测模型上进行改进。另一方面着眼于时序数据的特征学习,分析了时序数据特征提取与特征选择的常用算法,并由此提出了基于时序数据的特征学习框架。通过时序数据的特征学习框架与改进的Elman神经网络预测模型相结合,提出了一个互联网金融风险预测模型。本模型着眼于实际应用。主要内容包含如下:1、分析了神经网络模型在时间序列预测上的应用。包含了前向神经网络和反馈神经网络。主要针对Elman神经网络模型的研究,分析了其模型结构,各层神经元的特点,并修改了Elman神经网络训练算法,在误差计算中将历史数据按照与当前时间的远近赋予相应的权值,以及加入时序数据随机过程,提出了改进的Elman神经网络时间序列预测模型(GT-Elman),从而增强了Elman神经网络对时间序列的预测性能。2、分析了时序数据常用的特征提取算法与特征选择算法。通过将时域序列转化为频域序列如快速傅立叶变换,离散小波变换等特征提取算法,提取时序数据中的特征;在特征选择算法里分析了Clamping Network的网络结构,算法思想和缺陷,并根据该缺陷提出了一种改进的Clamping Network(DS-Clamping),从而增强了Clamping Network在特征选择上的性能。相比于直接使用原始时序数据作为输入,通过这种对时序数据特征学习模型得到时序数据的特征作为时间序列预测模型的输入,能更好的提高预测精度,提高系统的预测性能。3、针对互联网金融风险预测系统的设计和开发。本系统采用基于SpringMVC框架,结合Bootstrap、Echart、JQuery搭建了互联网金融风险预测系统。并可视化展示了系统返回的结果,结果表明本系统有着较好的实用价值。
[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.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F724.6;F832;TP183
本文编号：2340736
[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.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F724.6;F832;TP183
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