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Hierarchical Clustering of Time Series using Gaussian Mixture Models and Variational Autoencoders

Wilhelmsson, Per LU (2019) In Master's Theses in Mathematical Sciences FMSM01 20191
Mathematical Statistics
Abstract
This thesis proposes a hierarchical clustering algorithm for time series, comprised of a variational autoencoder to compress the series and a Gaussian mixture model to merge them into an appropriate cluster hierarchy. This approach is motivated by the autoencoders good results in dimensionality reduction tasks and by the likelihood framework given by the Gaussian mixture model. In contrast to similar clustering algorithms, this algorithm tries to answer the question of the true number of clusters in the data set and gives superior visualisation possibilities. Furthermore, the thesis shows how cluster analysis in general can be applied to several interesting problems in finance, and specifically how this algorithm is engineered to... (More)
This thesis proposes a hierarchical clustering algorithm for time series, comprised of a variational autoencoder to compress the series and a Gaussian mixture model to merge them into an appropriate cluster hierarchy. This approach is motivated by the autoencoders good results in dimensionality reduction tasks and by the likelihood framework given by the Gaussian mixture model. In contrast to similar clustering algorithms, this algorithm tries to answer the question of the true number of clusters in the data set and gives superior visualisation possibilities. Furthermore, the thesis shows how cluster analysis in general can be applied to several interesting problems in finance, and specifically how this algorithm is engineered to outperform standard clustering algorithms on these problems. (Less)
Popular Abstract
Machine learning is rapidly changing the financial industry. This thesis contributes by clustering financial assets using artificial neural networks. The goal is to enhance analysis and predict the future.
Please use this url to cite or link to this publication:
author
Wilhelmsson, Per LU
supervisor
organization
alternative title
Hierarkisk klustring av tidsserier med Gaussiska blandmodeller och variationsautoencoder
course
FMSM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Clustering, Deep Learning, Machine Learning, Time Series, Variational Autoencoders, Gaussian Mixture Models
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMS-3367-2019
ISSN
1404-6342
other publication id
2019:E27
language
English
id
8982213
date added to LUP
2019-10-08 13:58:50
date last changed
2019-10-08 13:58:50
@misc{8982213,
  abstract     = {{This thesis proposes a hierarchical clustering algorithm for time series, comprised of a variational autoencoder to compress the series and a Gaussian mixture model to merge them into an appropriate cluster hierarchy. This approach is motivated by the autoencoders good results in dimensionality reduction tasks and by the likelihood framework given by the Gaussian mixture model. In contrast to similar clustering algorithms, this algorithm tries to answer the question of the true number of clusters in the data set and gives superior visualisation possibilities. Furthermore, the thesis shows how cluster analysis in general can be applied to several interesting problems in finance, and specifically how this algorithm is engineered to outperform standard clustering algorithms on these problems.}},
  author       = {{Wilhelmsson, Per}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Hierarchical Clustering of Time Series using Gaussian Mixture Models and Variational Autoencoders}},
  year         = {{2019}},
}