余额宝资金流量预测方法研究

余额宝资金流量预测方法研究(任务书,开题报告,论文说明书12000字)
摘要
近年来，互联网金融迅猛发展，诞生了许多新事物，例如阿里巴巴集团蚂蚁金服推出了余额宝。从2013 年 6 月 13 日诞生至今，余额宝已经有着庞大的用户群体，每天数以亿计的资金流动，流动的项目数量和金额均极其庞大。因此，我们需要采用各种方法和模型，根据过去一段时间众多余额宝用户的资金流入和流出情况，相对准确地预测每天的资金流动情况，就可以在确保无资金流动危机的情况下，可用资金投资于其他领域,以最大限度地回报。分析(回归)是研究变量(由解释变量)对另一个变量(解释变量)的具体依赖关系的方法和理论。从一组样本数据出发,确定各种统计检验关系的可信度变量之间的数学关系,找出影响某一特定变量的变量的哪些变量不显著。利用关系式根据一个或多个变量的值来预测或控制另一个变量的值,并给出该预测或控制的精确程度。主要回归拟合方法为光滑曲线拟合,最小二乘法插值，多项式拟合，三角函数拟合，高斯曲线等，基于预测精度和实现的难度，我们采用多项式拟合，建立多项式回归模型。
多项式回归模型(Polynomial regression model,简记PNRM)，该模型是时间序列分析中的经典预测方法之一。本项目预测的具体步骤如下：

[资料来源：https://www.doc163.com]

1.    根据时间序列的散点图、自相关函数和偏自相关函数图识别其平稳性（仅取平稳序列作为历史数据）。
2.    根据所识别的特征，建立相应的时间序列模型。经过平稳化处理，逐步建立一次，二次，三次和四次多项式回归模型。
3.    利用已通过检验的模型对未来一个月每天的申购赎回情况进行预测。
计算所有用户在测试集上每天的申购及赎回总额与实际情况总额的误差，误差越小，预测精度越高。时间序列可能包含趋势,季节,周期中的部分或全部这三个组成部分,加上随机成分。为了减少误差,今后的改进方向是采用ARIMA模型进行建模和预测。
关键词：资金流量；时间序列分析；多项式回归模型；ARIMA；自相关函数
 
ABSTRACT
In recent years, the rapid development of internet finance, the birth of many new things, such as Alibaba Group Ant Gold clothing launched the balance treasure. Since the birth of June 13, 2013, the balance has a huge user base, hundreds of billions of dollars a day of capital flow, the flow of the number of projects and amounts are extremely large. Therefore, we need to adopt various methods and models, according to the financial inflow and outflow of many balance users over the past period, a relatively accurate forecast of the daily flow of funds can be used to ensure that the funds available are invested in other areas in order to maximize revenue in the absence of a crisis of financial flows.Regression analysis (Regression) is a computational method and theory that studies the specific dependencies of a variable (interpreted variable) on another (some) variable (explanatory variable). Starting from a set of sample data, the mathematical relation between variables is determined to carry out various statistical tests on the reliability of these relationships, and to find out which variables have significant impact and which are not significant from the variables that affect a given variable. The desired relationship is used to predict or control the value of another specific variable based on the value of one or more variables, and the accuracy of the prediction or control is given.The main regression fitting methods include smoothing curve fitting, least squares interpolation, polynomial fitting, trigonometric fitting, Gauss curve, and so on, based on the prediction precision and the difficulty of realization, we use polynomial fitting to establish polynomial regression model. [来源：http://Doc163.com]
Polynomial regression models (Polynomial Regression model, PNRM), which is one of the classical prediction methods in time series analysis. The specific steps of the project forecast are as follows:
1. According to the scatter graph, autocorrelation function and partial autocorrelation function graph of time series, the smoothness is identified (only stationary sequence is taken as historical data).
2. The corresponding time series model is established based on the identified features. After the stabilization process, a polynomial regression model was set up, two times, three times and four times.
3. Use the tested model to forecast the daily redemption of the next one months.
Calculates the error of daily purchase and redemption Total and actual value of all users on test set, the smaller the error, the higher the forecast accuracy. In order to reduce the error, the future improvement direction is to use the ARIMA model for modeling and forecasting.
Key Words: capital flow; time series analysis; PNRM model;ARIMA; autocorrelation function [资料来源：Doc163.com]

目录
第1章绪论    6
1.1 研究目的及意义    6
1.2 国内外研究现状    6
1.3 研究内容和目标    8
第2章技术路线及改进    9
2.1 数据预处理    9
2.1.1 原始时间序列    9
2.1.2 平稳性检验    10
2.2 拟采用的技术方案    10
2.3 分析与改进    12
第3章模型的建立与求解    14
3.1 模型的建立    14
3.1.1 数据的分析    14
3.1.2 多项式回归模型    16
3.2 模型的求解    16
3.2.1 平稳时间序列    16
3.2.2 数值计算求解    18
第4章结语    32
4.1 总结    32
4.1.1 思路概括    32
4.1.2 主要创新点    32
4.1.3 局限和不足    33
4.2 改进方向    33

[来源：http://www.doc163.com]

参考文献    34
致谢    36

[资料来源：https://www.doc163.com]