Hausdorff clustering of financial time series
Nicolas Basalto, Roberto Bellotti, Francesco De Carlo, Paolo Facchi,, Saverio Pascazio
TL;DR
This paper introduces a clustering method using Hausdorff distance to analyze financial time series, demonstrated on DJIA data, offering a new approach to understanding stock market behaviors.
Contribution
The paper presents a novel clustering technique based on Hausdorff distance specifically applied to financial time series data.
Findings
Effective clustering of DJIA time series
Hausdorff distance captures meaningful financial relationships
Method shows potential for financial data analysis
Abstract
A clustering procedure, based on the Hausdorff distance, is introduced and tested on the financial time series of the Dow Jones Industrial Average (DJIA) index.
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Time Series Analysis and Forecasting · Advanced Clustering Algorithms Research