diplomsko delo
Nina Velikajne (Author), Miha Moškon (Mentor)

Abstract

V okviru diplomskega dela smo vzpostavili metodologijo za analizo ritmične pojavnosti dogodkov. Implementirali smo zbirko funkcij, ki jih lahko neposredno uporabimo pri analizi cirkadianih števnih podatkov. Za analizo tovrstnih podatkov moramo združiti metode za detekcijo ritma z metodami za števne podatke. Za detekcijo ritma in transformacijo vhodnih podatkov smo uporabili metodo cosinor. Implementirana računska metoda dovoljuje tudi poljubno nastavljanje števila komponent. Za analizo števnih podatkov in reševanje regresijskega problema smo uporabili pet računskih modelov, in sicer Poissonov model, generaliziran Poissonov model, Poissonov model z inflacijo ničel, negativen binomski model in negativen binomski model z inflacijo ničel. Vzpostavljena metoda omogoča primerjavo in iskanje najbolj ustreznega računskega modela z optimalnim številom komponent. Vsebuje tudi funkcije, ki omogočajo primerjavo ritma v odvisnosti od različnih faktorjev. Celotna računska metoda je bila testirana na dveh prometnih podatkovnih zbirkah, ki so nam jih posredovali z Ministrstva za infrastrukturo.

Keywords

števni podatki;ritmični podatki;cirkadiani ritem;cosinor;pojavnost dogodkov;regresija;promet;računalništvo;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [N. Velikajne]
UDC: 004(043.2)
COBISS: 50587907 Link will open in a new window
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Other data

Secondary language: English
Secondary title: Overview and application of computational approaches for the analysis of rhythmicity in count data
Secondary abstract: In the thesis we have established a methodology for the analysis of rhythmicity in count data. We have implemented a set of functions, that we can use directly for the analysis of circadian count data. To analyse this type of data, we need to combine methods for rhythmicity detection with methods for analysing count data. For the purpose of rhythmicity detection and transformation of input data, we have used the cosinor method. The implemented computational method allows to identify the number of components automatically. For the analysis of the count data and the solution of the regression problem we have used five computational models -- Poisson model, generalized Poisson model, zero-inflated Poisson model, negative binomial model, and zero-inflated negative binomial model. The established method allows us to compare and find the most suitable model with the optimal number of components. The method also includes functions to compare the rhythm in dependence of different factors. The complete method was tested on two traffic datasets obtained from the Ministry of Infrastructure of Republic of Slovenia.
Secondary keywords: count data;rhythmic data;circadian rhythm;cosinor;event occurrence;regression;traffic;computer science;computer and information science;diploma;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 45 str.
ID: 12534755