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Title:Klasifikacija dogodkov v časovnih vrstah s strojnim učenjem
Authors:Kavran, Domen (Author)
Lukač, Niko (Mentor) More about this mentor... New window
Files:.pdf UN_Kavran_Domen_2018.pdf (979,03 KB)
MD5: B35CBB4859C201A824717EC475933DB8
 
Language:Slovenian
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu opišemo algoritem segmentacije časovnih vrst in postopek priprave vektorjev značilnic segmentov za učenje in testiranje klasifikacijskih modelov za zaznavo dogodkov. Segmentacijo časovnih vrst izvedemo z algoritmom drsečega okna, kjer za merilo razdalje med vrednostmi uporabimo algoritem dinamičnega časovnega sledenja. Pripravo vektorjev značilnic segmentov začnemo z definiranjem slovarja lokalnih podsegmentov. Slovar je pridobljen z gručenjem K-povprečij. Vsak segment predstavimo z normaliziranim histogramom pojavitev lokalnih podsegmentov na podlagi slovarja. Za učenje klasifikacijskih modelov uporabimo algoritme strojnega učenja, ki se razlikujejo v računski zahtevnosti in doseženi natančnosti, na katero vplivajo tudi izbrani parametri segmentacije in velikost slovarja.
Keywords:klasifikacija, časovna vrsta, strojno učenje, segmentacija
Year of publishing:2018
Publisher:D. Kavran
Source:[Maribor
UDC:004.5:004.852(043.2)
COBISS_ID:21746198 New window
NUK URN:URN:SI:UM:DK:IAHD3P8D
Views:1193
Downloads:150
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:26.07.2018

Secondary language

Language:English
Title:Classification of events in time series data using machine learning
Abstract:In this thesis, an algorithm of time series segmentation and procedure of preparing segments feature vectors for training and testing classification models are presented, in order to detect time series events. Sliding window algorithm with dynamic time warping as distance measure is used for time series segmentation. Creating segments feature vectors starts with defining a dictionary of local subsegments. Dictionary is created with K-means clustering. Each segment is described with normalized histogram of local subsegment occurances based on dictionary. Machine learning algorithms, used for training classification models, differ in computation complexity and achieved accuracy. Achieved accuracy depends on the selected segmentation parameters and dictionary.
Keywords:classification, time series, machine learning, segmentation


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