基于导向随机狼群算法的多元时间序列变量选择研究

项目名称： 基于导向随机狼群算法的多元时间序列变量选择研究

项目编号： No.61502534

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 吴虎胜

作者单位： 中国人民武装警察部队工程大学

项目金额： 21万元

中文摘要： 多元时间序列变量选择是多个领域的基础难题。针对该科学问题中存在的学习模式多样、优化模型复杂、变量维度高、关系复杂等问题，研究多元时间序列变量子集的评价准则，并重点突破具有高度非线性和NP-Hard特性的最优变量子集搜索问题。对于前者，针对多元时间序列的矩阵形式特点，以监督/无监督学习为主线，基于多元时间序列相似性度量分别建立多元时间序列相似性匹配模型和聚类集成模型，进行多元时间序列变量子集评价准则的研究；对于后者，从启发式信息导向与随机策略间关系的角度研究高维组合优化，力图提出一种具有普适性理论指导意义的“导向随机”优化机制，并进行导向随机狼群算法的改进和理论分析研究，进而建立基于狼群算法的多元时间序列变量选择模型；最后，结合大量标准算例和实际多元时间序列数据，开展性能验证与应用研究。预期成果将为多元时间序列数据挖掘和群体智能优化提供新思路和新方法，具有非常好的研究意义和广泛的应用前景。

中文关键词： 群体智能；组合优化；多元时间序列；变量选择；狼群算法

英文摘要： How to select variable for multivariate time series is a difficult yet fundamental problem in many fields. However, this scientific problem exists a lot of difficulties, including multiple learning mode, complex optimization model, high dimensional variables and complex relationship between them. In this project, the evaluation criteria of variable subsets are researched and the key breakthrough is the search problem of optimal variable subset, which has the highly nonlinear and NP-Hard characteristics. For the former, according to the matrix form characteristics of multivariate time series and following the main line of supervised/unsupervised learning, multivariate time series similarity matching model and cluster integration model are respectively established based on similarity measurement of multivariate time series. For the latter, firstly, the study on the high dimensional combinatorial optimization is conducted from the view of the relationship between heuristic information guiding and stochastic strategies, and a ‘guided-stochastic’ optimization mechanism which has the universal theoretical guidance significance is tentatively proposed; Secondly, study on the improvement and theoretical analysis about the guided-stochastic wolf pack algorithm, to further improve;the performance of the algorithm and enrich the theoretical basis, and then establish the variable selection model based on wolf pack algorithm. Finally, the performance verification and application research among theory, algorithm and the model is studied based on a large number of benchmarks and actual multivariate time series data. Expected results will provide new ideas and methods for swarm intelligence optimization and multivariate time series data mining, and have good research significance and broad application prospect.

英文关键词： swarm intelligence;combinatorial optimization;multivariate time series;variable selection;wolf pack algorithm