聚类分析
动态时间归整
计算机科学
数据挖掘
多元统计
模糊聚类
离群值
波动性聚类
计量经济学
文件夹
异常检测
时间序列
财务
波动性(金融)
人工智能
数学
经济
机器学习
ARCH模型
作者
Pierpaolo D’Urso,Livia De Giovanni,Riccardo Massari
标识
DOI:10.1007/s10479-019-03284-1
摘要
In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time warping distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers.
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