分布型符号数据分析为处理和分析大规模数据提供了一种全新而有效的思想，在工程技术、经济管理、金融风险等领域都有很广泛的应用前景。本项目以金融领域的海量时序数据分析为应用背景，探索带有时序特征的分布型符号数据的建模理论和技术方法。针对现有分布型符号数据代数运算不精确和不封闭的问题，本项目首先从分布型符号数据的代数基础和运算规则入手，建立满足概率空间中的约束条件和基本假设的分布型符号数据的完备代数空间。在此基础上，系统地分析分布型符号数据的一维时间序列模型和多维面板模型的建模理论和估计方法。结合金融领域的实际数据，拟开展基于高频数据的中国股市波动特征和预测研究的实证分析。这些研究的实现，为大数据环境下的复杂系统时序数据的建模和预测提供一种新的更加有效的分析工具。

Distributional symbolic data analysis provides a new and effective idea for processing and analyzing large-scale data. It has a wide range of applications in engineering, economic management, financial risk and other fields. This project uses massive time series data analysis in the financial field as the application background and explores distributional symbolic data modeling theory and technical methods with time-series features. In allusion to the problem of the algebraic operation of the existing distributional symbolic data being inaccurate and non-closed, this project starts with the algebraic basis and operational rules of the distributional symbolic data, and establishes the algebraic space of distributional symbolic data that satisfies the constraints and basic assumptions in the probability space. On this basis, the modeling theory and estimation method of one-dimensional time series model and multi-dimensional panel model of distributional symbolic data are systematically analyzed. Combined with the actual data in the financial field, it is proposed to carry out an empirical analysis of the fluctuation characteristics and forecasting of the stock market based on high-frequency data. The implementation of these studies provides a new and more effective analysis tool for modeling and predicting time series data of complex systems in the context of big data.