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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Expert systems with applications Ročník 162; s. 113868
Hlavní autoři: Majumdar, Sourav, Laha, Arnab Kumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 30.12.2020
Elsevier BV
Témata:
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 přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 classification method RF-TDA based on TDA. Using SOM-TDA we examine the topological similarities and dissimilarities of some well-known time-series models used in finance. We also use the RF-TDA to examine if the topological features can be used to distinguish between time series models using simulated data. The performance of RF-TDA on the classification task is compared against three other classification methods. We also consider an application of RF-TDA to financial time series classification using real-life price data of stocks belonging to different sectors. RF-TDA is seen to perform quite well in the two experiments based on real-life stock-price data. This implies that the topological features of the time series of stock prices in the different sectors are not identical and have distinctive features that can be discerned through the use of TDA. We also briefly consider multi-class classification using RF-TDA. •New methods for time series clustering (SOM-TDA) and classification (RF-TDA).•RF-TDA outperforms other methods on the classification task.•Dependence of stock price movements on sectors in NSE is revealed using RF-TDA.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113868