Clustering and classification of time series using topological data analysis with applications to finance

In this paper, we propose new methods for time series classification and clustering. These methods are based on techniques of Topological Data Analysis (TDA) such as persistent homology and time delay embedding for analysing time-series data. We present a new clustering method SOM-TDA and a new clas...

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Vydané v:	Expert systems with applications Ročník 162; s. 113868
Hlavní autori:	Majumdar, Sourav, Laha, Arnab Kumar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 30.12.2020 Elsevier BV
Predmet:	Classification Clustering Data analysis Finance Homology Persistent homology Random forest Self organizing maps Takens theorem Time delay embedding Time lag Time series Topology Random forest Persistent homology Self organizing maps Time delay embedding Takens theorem
ISSN:	0957-4174, 1873-6793
On-line prístup:	Získať plný text
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