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dc.contributor.author
Tsolakis, Efstathios
en
dc.date.accessioned
2020-06-15T10:27:11Z
dc.date.available
2020-06-16T00:00:42Z
dc.date.issued
2020-06-15
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29596
dc.rights
Default License
dc.subject
Telecommunications
en
dc.subject
Traffic algorithms
en
dc.title
Forecasting Traffic Algorithms in Telecommunications
en
heal.type
masterThesis
en_US
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
2020-05-20
heal.abstract
This dissertation investigates time-series analysis on Telecommunication Traffic data. The data set is comprised of two time-series of 4G-LTE technology, where one represents the Downlink and the other the Uplink speed for one Base Station. The time series has an hourly frequency and the training set has a total of thirty-seven (37) days and the test set up to sixteen (16) days. We initially perform a Visual and Statistical exploration to gain insight for applying the according algorithms. Data preprocess was necessary, since we have real-life data, where missing and erroneous values occur. Also, a look into statistical and econometric approaches takes place, in order to find the optimal algorithms with respect to the special characteristics of the given data. Briefly, the dissertation describes the procedure for using Python as the mean to efficiently and accurately provide forecasts on a time series to gain knowledge for effective management and planning for a Telecommunication network. Lastly, the data set used was for the months February to April of 2014, in Greece.
en
heal.advisorName
Baltagiannis, Agamemnon
en
heal.committeeMemberName
Baltatzis, Dimitrios
en
heal.committeeMemberName
Stavrinides, Stavros
el
heal.committeeMemberName
Baltagiannis, Agamemnon
el
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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