dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Shafiee Kamalabad, Mahdi | |
dc.contributor.author | Besselink, Bradley | |
dc.date.accessioned | 2022-08-04T00:00:35Z | |
dc.date.available | 2022-08-04T00:00:35Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/42115 | |
dc.description.abstract | This paper proposes a strategy to study the social dynamics in timestamped data. The
analysis is based on the Relational Event History (REH). It introduces Apollo 13 data to
illustrate the study objective. The goal of this study is to detect change points in relational
event history data based on moving window approach with optimal window length. One
of the methods used in the study is the Relation Event Model. The Relational Event model
is a method to study social networks over time. This method is later on complemented
with the more dynamic method called the moving window approach. The results of the
moving window approach, expressed in parameters, are the input variables for the next
method called change point detection. Change point detection is introduced to study the
change in parameters over the different windows, to analyze the social interaction patterns
that can be found in the Apollo 13 data. Data cleaning was performed on the Apollo 13
dataset to make it suitable for the analysis. The analysis is performed in R. Seven effects
are proposed to fit the Relational Event Model. Five moving window lengths are
considered in order to find the optimal window size. These window sizes are
complimented by three different overlapping percentages. Change points are detected
using the ‘MedoidAI’ application. ‘MedoidAI’ is an R/Shiny app for time-series
segmentation and changepoint detection tasks. In the application two different algorithms
are considered to detect multiple change points. These algorithms are the Binary
Segmentation and the PELT algorithm. Both are considered and compared in their ability
to detect change points. The results of the study show an optimal window length at 0.5
hours with 50 percent overlap. This window length showed the lowest BIC score. The
optimal window can detect change points that are more specific than the results seen at a
larger window size. While the smaller window size showed a results that seems more
sensitive to the datapoints over time. This implies that the optimal window could be the
optimal size to detect change points in the Apollo 13 dataset. | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | This study applies the Relational Event Model in combination with the Moving Window Approach to detect changepoints in Relational Event History Data. The aim of the study is to propose a strategy to find the optimal window lenght to study time staped events. Data cleaning and analysis are performed on the transcriptions of the interactions between the ground and air team of the Apollo 13. | |
dc.title | Change point detection in relational event history data based on moving window approach with optimal window length. | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Relational Event Model;REM;Relational Event History;Moving Window;Change point detection | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 7650 | |