Time series clustering and anomaly detection in the financial markets

Time series clustering and anomaly detection provide researches with useful domain insights but are also two of the most challenging time series data mining issues. As both activities have high time complexity cost and high memory requirements, few studies on large time series datasets have been mad...

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Main Author: Lim, Wilbur Yong Wei
Other Authors: Ke Yiping, Kelly
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/148473
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1484732021-05-04T03:00:05Z Time series clustering and anomaly detection in the financial markets Lim, Wilbur Yong Wei Ke Yiping, Kelly School of Computer Science and Engineering ypke@ntu.edu.sg Engineering::Computer science and engineering Time series clustering and anomaly detection provide researches with useful domain insights but are also two of the most challenging time series data mining issues. As both activities have high time complexity cost and high memory requirements, few studies on large time series datasets have been made. In this paper, we focus on unsupervised whole time series clustering, identifying anomalous clusters, and point outlier anomaly detection within time series sequences within the financial markets. Using K Means ++ and K Shape, Whole time series clustering is performed on the NYSE, NASDAQ and AMEX stock companies’ stock price performance between the period 1st October 2005 to 1st October 2020, a 15-year-long time period. The dataset reviewed consists of 11 market sectors under the GICS classification methodology and thus includes over 2785 individual time series. This review is arguably one of the few, if not the first, to evaluate whole time series clustering on such a large scale. After evaluating anomalous clusters found in the dataset, two unsupervised point-outlier detection algorithms, namely Isolation Forest, and One Class Support Vector Machine, will be employed on the same dataset before the detection results between the two algorithms are compared. Bachelor of Engineering (Computer Science) 2021-05-04T03:00:05Z 2021-05-04T03:00:05Z 2021 Final Year Project (FYP) Lim, W. Y. W. (2021). Time series clustering and anomaly detection in the financial markets. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148473 https://hdl.handle.net/10356/148473 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Lim, Wilbur Yong Wei
Time series clustering and anomaly detection in the financial markets
description Time series clustering and anomaly detection provide researches with useful domain insights but are also two of the most challenging time series data mining issues. As both activities have high time complexity cost and high memory requirements, few studies on large time series datasets have been made. In this paper, we focus on unsupervised whole time series clustering, identifying anomalous clusters, and point outlier anomaly detection within time series sequences within the financial markets. Using K Means ++ and K Shape, Whole time series clustering is performed on the NYSE, NASDAQ and AMEX stock companies’ stock price performance between the period 1st October 2005 to 1st October 2020, a 15-year-long time period. The dataset reviewed consists of 11 market sectors under the GICS classification methodology and thus includes over 2785 individual time series. This review is arguably one of the few, if not the first, to evaluate whole time series clustering on such a large scale. After evaluating anomalous clusters found in the dataset, two unsupervised point-outlier detection algorithms, namely Isolation Forest, and One Class Support Vector Machine, will be employed on the same dataset before the detection results between the two algorithms are compared.
author2 Ke Yiping, Kelly
author_facet Ke Yiping, Kelly
Lim, Wilbur Yong Wei
format Final Year Project
author Lim, Wilbur Yong Wei
author_sort Lim, Wilbur Yong Wei
title Time series clustering and anomaly detection in the financial markets
title_short Time series clustering and anomaly detection in the financial markets
title_full Time series clustering and anomaly detection in the financial markets
title_fullStr Time series clustering and anomaly detection in the financial markets
title_full_unstemmed Time series clustering and anomaly detection in the financial markets
title_sort time series clustering and anomaly detection in the financial markets
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148473
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