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DKUM
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Title:
Napovedovanje multivariatnih časovnih vrst geoprostorskih podatkov z uporabo konvolucijsko-povratnih nevronskih mrež : magistrsko delo
Authors:
ID
Uremović, Niko
(
Author
)
ID
Lukač, Niko
(
Mentor
)
More about this mentor...
ID
Bizjak, Marko
(
Comentor
)
Files:
MAG_Uremovic_Niko_2022.pdf
(2,42 MB)
MD5: DE3BE96E41A21A69870E5663A9C79576
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu predstavimo nov pristop za napovedovanje multivariatnih časovnih vrst geoprostorskih podatkov. Pripravimo pregled obstoječih pristopov k napovedovanju časovnih vrst prostorskih podatkov. Predstavimo koncepte na katerih temelji konvolucijsko-povratna nevronska mreža ConvLSTM in njeno teoretično osnovo. Z uporabo ConvLSTM pri napovedovanju upoštevamo tako časovne odvisnosti med spremenljivkami, kot tudi prostorske odvnisnosti med podatki v sosednjih točkah. Metodo preizkusimo na primeru napovedovanja več spremenljivk onesnaženosti zraka za več merilnih postaj na različnih lokacijah in jo primerjamo s sorodnimi deli.
Keywords:
Multivariatne časovne vrste
,
geoprostorski podatki
,
napovedovanje časovnih vrst
,
konvolucijsko-povratne nevronske mreže
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[N. Uremović]
Year of publishing:
2022
Number of pages:
1 spletni vir (1 datoteka PDF (VII, 36 f.))
PID:
20.500.12556/DKUM-82698
UDC:
519.2:004.8(043.2)
COBISS.SI-ID:
132919555
Publication date in DKUM:
21.10.2022
Views:
757
Downloads:
85
Metadata:
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:
30.08.2022
Secondary language
Language:
English
Title:
Geospatial multivariate time series forecasting using convolutional reccurrent neural networks
Abstract:
In this thesis we present a method for multivariate time series forecasting for geospatial data. We prepare an overview of existing methods for multivariate spatial time series forecasting. We present the theorethical background of the ConvLSTM neural network architecture and the concepts it is based on. By using ConvLSTM for geospatial time series forecasting, we account for both spatial and temporal dependencies in our data. We test the proposed method on the case of forecasting multiple variables of air pollution for multiple measurement stations and compare our results to related work.
Keywords:
Multivariate time series
,
geospatial data
,
time series forecasting
,
convolutional recurrent neural networks
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