Topological data analysis for identifying critical transitions in cryptocurrency time series

P. Saengduean; S. Noisagool; F. Chamchod

doi:10.1109/IEEM45057.2020.9309855

Topological data analysis for identifying critical transitions in cryptocurrency time series

P. Saengduean
, S. Noisagool
, F. Chamchod

Mahidol University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

In this study, we investigate financial crashes in the cryptocurrency market including both mini and major crashes for two cryptocurrencies, Bitcoin and Ethereum, during the period that the digital market crashed in 2018. By applying techniques in topological data analysis, we are able to predict financial transitions and explore optimal values of the window size and the dimension of point cloud data to obtain good early warning signals. Our results demonstrate good early warning signals before the financial crashes and also show that the L1-norm and C1 - norm of persistent landscapes peak before the crashes occur.

Original language	English
Title of host publication	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
Publisher	IEEE Computer Society
Pages	933-938
Number of pages	6
ISBN (Electronic)	9781538672204
DOIs	https://doi.org/10.1109/IEEM45057.2020.9309855
Publication status	Published - 14 Dec 2020
Externally published	Yes
Event	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 - Virtual, Singapore, Singapore Duration: 14 Dec 2020 → 17 Dec 2020

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2020-December
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020
Country/Territory	Singapore
City	Virtual, Singapore
Period	14/12/20 → 17/12/20

Keywords

Cryptocurrency
Financial crashes
Financial time series
K-means clustering
Topological data analysis

Access to Document

10.1109/IEEM45057.2020.9309855

Cite this

Saengduean, P., Noisagool, S., & Chamchod, F. (2020). Topological data analysis for identifying critical transitions in cryptocurrency time series. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 (pp. 933-938). Article 9309855 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2020-December). IEEE Computer Society. https://doi.org/10.1109/IEEM45057.2020.9309855

Saengduean, P. ; Noisagool, S. ; Chamchod, F. / Topological data analysis for identifying critical transitions in cryptocurrency time series. 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020. IEEE Computer Society, 2020. pp. 933-938 (IEEE International Conference on Industrial Engineering and Engineering Management).

@inproceedings{654c65c92262418e8b7e7569055262a4,

title = "Topological data analysis for identifying critical transitions in cryptocurrency time series",

abstract = "In this study, we investigate financial crashes in the cryptocurrency market including both mini and major crashes for two cryptocurrencies, Bitcoin and Ethereum, during the period that the digital market crashed in 2018. By applying techniques in topological data analysis, we are able to predict financial transitions and explore optimal values of the window size and the dimension of point cloud data to obtain good early warning signals. Our results demonstrate good early warning signals before the financial crashes and also show that the L1-norm and C1 - norm of persistent landscapes peak before the crashes occur.",

keywords = "Cryptocurrency, Financial crashes, Financial time series, K-means clustering, Topological data analysis",

author = "P. Saengduean and S. Noisagool and F. Chamchod",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020 ; Conference date: 14-12-2020 Through 17-12-2020",

year = "2020",

month = dec,

day = "14",

doi = "10.1109/IEEM45057.2020.9309855",

language = "English",

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Saengduean, P, Noisagool, S & Chamchod, F 2020, Topological data analysis for identifying critical transitions in cryptocurrency time series. in 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020., 9309855, IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2020-December, IEEE Computer Society, pp. 933-938, 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020, Virtual, Singapore, Singapore, 14/12/20. https://doi.org/10.1109/IEEM45057.2020.9309855

Topological data analysis for identifying critical transitions in cryptocurrency time series. / Saengduean, P.; Noisagool, S.; Chamchod, F.
2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020. IEEE Computer Society, 2020. p. 933-938 9309855 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2020-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Topological data analysis for identifying critical transitions in cryptocurrency time series

AU - Saengduean, P.

AU - Noisagool, S.

AU - Chamchod, F.

PY - 2020/12/14

Y1 - 2020/12/14

N2 - In this study, we investigate financial crashes in the cryptocurrency market including both mini and major crashes for two cryptocurrencies, Bitcoin and Ethereum, during the period that the digital market crashed in 2018. By applying techniques in topological data analysis, we are able to predict financial transitions and explore optimal values of the window size and the dimension of point cloud data to obtain good early warning signals. Our results demonstrate good early warning signals before the financial crashes and also show that the L1-norm and C1 - norm of persistent landscapes peak before the crashes occur.

AB - In this study, we investigate financial crashes in the cryptocurrency market including both mini and major crashes for two cryptocurrencies, Bitcoin and Ethereum, during the period that the digital market crashed in 2018. By applying techniques in topological data analysis, we are able to predict financial transitions and explore optimal values of the window size and the dimension of point cloud data to obtain good early warning signals. Our results demonstrate good early warning signals before the financial crashes and also show that the L1-norm and C1 - norm of persistent landscapes peak before the crashes occur.

KW - Cryptocurrency

KW - Financial crashes

KW - Financial time series

KW - K-means clustering

KW - Topological data analysis

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U2 - 10.1109/IEEM45057.2020.9309855

DO - 10.1109/IEEM45057.2020.9309855

M3 - Conference contribution

AN - SCOPUS:85099748967

T3 - IEEE International Conference on Industrial Engineering and Engineering Management

SP - 933

EP - 938

BT - 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020

PB - IEEE Computer Society

T2 - 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020

Y2 - 14 December 2020 through 17 December 2020

ER -

Saengduean P, Noisagool S , Chamchod F. Topological data analysis for identifying critical transitions in cryptocurrency time series. In 2020 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2020. IEEE Computer Society. 2020. p. 933-938. 9309855. (IEEE International Conference on Industrial Engineering and Engineering Management). doi: 10.1109/IEEM45057.2020.9309855

Topological data analysis for identifying critical transitions in cryptocurrency time series

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