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Title:INFORMACIJSKA PODPORA NAPOVEDOVANJU ZNAČILNOSTI PROMETA NA PODLAGI ČASOVNIH SERIJ PODATKOV
Authors:Sevčnikar, Andrej (Author)
Welzer-Družovec, Tatjana (Mentor) More about this mentor... New window
Files:.pdf MAG_Sevcnikar_Andrej_2013.pdf (3,55 MB)
 
Language:Slovenian
Work type:Master's thesis (m2)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu smo se osredotočili na napovedovanje značilnosti prometa na osnovi podatkov časovnih vrst. Za dosego cilja smo v magistrski nalogi proučili področje integracije podatkov in podatkovnih skladišč, z uporabo katerega smo izdelali celovit pogled na podatke, ki je nujno potreben za nadaljnjo manipulacijo s podatki. Osrednja tematika magistrske naloge so modeli za napovedovanje podatkov časovnih vrst, s katerimi smo napovedovali gostoto prometa, pri tem pa preverjali napovedno napako. Določili smo najbolj optimalne modele za napovedovanje gostote prometa. Pokazali smo, da je pravilna predpriprava podatkov oziroma učenje modelov na podlagi dobro zasnovanih časovnih okvirjev izredno pomembno za pravilno napoved prometa, hkrati pa določili optimalno število časovnih okvirjev, pri katerih je napovedna napaka še sprejemljiva.
Keywords:podatkovno skladišče, integracija podatkov, časovne vrste, podatkovno rudarjenje, gručenje, promet, napovedovanje
Year of publishing:2013
Publisher:[A. Sevčnikar]
Source:Maribor
UDC:004.62(043)
COBISS_ID:17414678 Link is opened in a new window
NUK URN:URN:SI:UM:DK:IAGSA40U
Views:1096
Downloads:121
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:INOFMRATION SUPPORT FOR TRAFFIC CHARACTERISTIC PREDICTION BASED ON TIME SERIES DATA
Abstract:In the master thesis we focus on the prediction of traffic characteristics based on time series data. To achieve this goal we investigate areas of data integration and data warehousing in order to attain a comprehensive view of data which is vital for further data manipulation. The main area of this thesis are models for the prediction of time series data which make it possible to predict traffic whilst checking the predictive error. We determine the optimal model for predicting the density of traffic. We show that the learning models based on well-designed time frames are very important for precise traffic prediction. Additionally, we define the optimal number of time frames in which the prediction error is acceptable.
Keywords:data warehouse, data integration, time series, data mining, clustering, traffic, forecasting


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