Sequence Clustering for Regime Change Detection

这项资助申请旨在建立我们的研究小组和加拿大维度研究之间的研究伙伴关系。该研究项目建议研究使用先进的数据挖掘技术来检测金融时间序列数据的状态变化。我们将设计、实施和试验新的方法，以获得一套丰富的金融制度特征。该方法将基于我们最近的基于模型的分类序列聚类（MCSC）算法。它包含以下步骤：1)通过烛台表示金融时间序列，并进行聚类分析，将烛台表示转换为分类表示；2)使用MCSC算法进行分层序列聚类；3)探索MCSC的变阶马尔可夫模型提取签名模式；4)通过预测市场回报的比较研究，调查这些模式在描述制度特征方面的有用性。该方法的创新之处在于它采用无监督学习方法来分析制度和制度变化。它有潜力克服传统方法的主要限制。它将允许发现长期和统计上显著的模式，产生更具预测性和更容易解释的特征。该项目预计将通过扩大其现有平台的范围以及如何开发用于复杂时间序列分析的未来产品的内部知识，为我们的行业合作伙伴提供直接的经济效益。总的来说，它也有可能使工业、公共服务和科学研究部门受益。

This Engage grant application aims to build a research partnership between our research group andDimensional Research Canada. The research project proposes to investigate the use of advanced data miningtechniques for detecting regime changes from financial time series data. We will design, implement and test anew method in order to obtain a set of rich features characterizing financial regimes. The method will be basedon our recent Model-based Categorical Sequence Clustering (MCSC) algorithm. It contains the followingsteps: 1) representing financial time series via candlesticks and performing cluster analysis to convertcandlestick representation to categorical representation; 2) performing hierarchical sequence clustering usingthe MCSC algorithm; 3) exploring the variable-order Markov model of the MCSC to extract signature patterns;and 4) investigating the usefulness of these patterns to characterize regimes through a comparative study forpredicting market returns. The innovative nature of the proposed methodology lies in its unsupervised learningapproach to analyzing regimes and regime changes. It has the potential to overcome the major limitations of theconventional approaches. It will allow the discovery of long and statistically significant patterns, yielding morepredictive and easier-to-interpret features. The project is expected to provide direct economic benefits to ourindustry partner, by expanding the scope of its existing platform and its internal knowledge of how to developfuture products for complex time series analysis. It also has the potential to benefit the industry, public serviceand scientific research sectors in general.