时间序列应用毕业论文

Abstract Data mining is a database application and research of a new area, its objectives through the analysis of historical data, statistics of interest to users or valuable information. In stock trading business, every day large amounts of data into the data warehouse, the data on our understanding of market trends, make the right investment decision-making provides a theoretical basis for research. With time series analysis of theory and research methods to gradually mature, in the stock analysis of the forecast of time series analysis has become a practical significance. From the perspective of applications, data mining on the related concepts, through the time-series data analysis and processing, designed to achieve a stock transaction price forecast for the objectives of the intelligent data mining system. The system background SQL Server 2005 using the available time-series data for the pretreatment, and then build on these time-series data mining model. Outlook for the use of C # language design system interface, users need only a simple operation can view the time series mining model, and use the model to predict the stock transaction price. This study proposed the adoption of our history to predict the future provide a favourable environment for : data mining, time series analysis; Microsoft timing algorithm; time series mining model;

时间序列好发论文。根据查询相关公开信息资料显示，从系统论的角度看，时间序列就是某一系统在不同时间（地点、条件等）的响应，围绕时间序列预测、分类、异常检测、表示学习以及在医疗、生物、交通、音乐、金融等方向的应用。

Abstract Data mining is a database application and research of a new area, its objectives through the analysis of historical data, statistics of interest to users or valuable information. In stock trading business, every day large amounts of data into the data warehouse, the data on our understanding of market trends, make the right investment decision-making provides a theoretical basis for research. With time series analysis of theory and research methods to gradually mature, in the stock analysis of the forecast of time series analysis has become a practical significance. From the perspective of applications, data mining on the related concepts, through the time-series data analysis and processing, designed to achieve a stock transaction price forecast for the objectives of the intelligent data mining system. The system background SQL Server 2005 using the available time-series data for the pretreatment, and then build on these time-series data mining model. Outlook for the use of C # language design system interface, users need only a simple operation can view the time series mining model, and use the model to predict the stock transaction price. This study proposed the adoption of our history to predict the future provide a favourable environment for evidence. Keyword: data mining, time series analysis; Microsoft timing algorithm; time series mining model; Stock prices were forecast;