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dc.contributor.author
Lampada, Kyriaki
en
dc.date.accessioned
2022-06-10T10:24:14Z
dc.date.available
2022-06-10T10:24:14Z
dc.date.issued
2022-06-10
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29949
dc.rights
Default License
dc.subject
Time series analysis
el
dc.title
Network-based Time series Analysis
en
heal.type
masterThesis
en_US
heal.creatorID.email
klampada@ihu.edu.gr
heal.dateAvailable
2022-05-25
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2022-01-28
heal.abstract
This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. As Machine Learning establishes its way through every sector of our lives over the last decades, its utilization on Time Series analysis has been crucial. Machine Learning for forecasting the future behavior depending on past time series data, is being used widely in Economics, Science, Medicine and Business sectors. Transforming time series into networks and applying graph theory and complex network theory on it, is a method used increasingly over the two past decades, since it combines the extensive research on these fields. The goal of this dissertation is to provide a comparative review of the time series models that are widely used and also present network-based time series analysis methods commonly used in the research field. The practical part of this paper uses a time series that consists of hourly power price and demand data, for the area of Sardegna over the period of one year.
el
heal.advisorName
Stavrinides, Stavros
el
heal.committeeMemberName
Stavrinides, Stavros
en
heal.committeeMemberName
Rantos, Konstantinos
en
heal.committeeMemberName
Baltatzis, Dimitrios
en
heal.academicPublisher
IHU
el
heal.academicPublisherID
ihu
en_US


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