Sequence Clustering for Regime Change Detection

这个参与补助金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。该研究项目建议调查使用先进的数据挖掘技术，从金融时间序列数据中检测制度变化。我们将设计，实施和测试新的方法，以获得一组丰富的功能特点的金融制度。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）通过蜡烛图表示金融时间序列并进行聚类分析，将蜡烛图表示转换为分类表示; 2）使用MCSC算法进行层次序列聚类; 3）探索MCSC的可变阶马尔可夫模型以提取签名模式; 4）通过比较研究来调查这些模式对预测市场回报率的有用性。所提出的方法的创新性在于其无监督学习的方法来分析政权和政权的变化。它有可能克服传统方法的主要局限性。它将允许发现长的和统计上显著的模式，产生更多的预测性和更容易解释的特征。该项目预计将为我们的行业合作伙伴提供直接的经济效益，通过扩大其现有平台的范围及其如何开发未来复杂时间序列分析产品的内部知识。它还具有使工业、公共服务和科学研究部门普遍受益的潜力。

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.