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Title:
Napovedovanje porabe pitne vode z metodami časovnih vrst in strojnega učenja : diplomsko delo
Authors:
ID
Dodič, Sara
(
Author
)
ID
Strnad, Damjan
(
Mentor
)
More about this mentor...
Files:
UN_Dodic_Sara_2023.pdf
(1,83 MB)
MD5: 4C3237DEF77BECC849880BFBD7021DEE
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V diplomski nalogi se ukvarjamo z napovedovanjem porabe pitne vode. Naš glavni cilj je primerjava napovednih modelov SARIMA in nevronske mreže LSTM. Pri napovedovanju se osredotočimo na časovne vrste posameznih gospodinjstev ter časovne vrste vodovodnega omrežja, ki so vzorčene mesečno. Primerjavo napovednih modelov izvedemo na podlagi njihove srednje kvadratne napake pri prileganju na časovno vrsto in napovedovanju porabe pitne vode na testni množici. Rezultati pokažejo, da se najbolje obnese model ARIMA.
Keywords:
časovne vrste
,
napovedovanje
,
ARIMA
,
LSTM
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[S. Dodič]
Year of publishing:
2023
Number of pages:
1 spletni vir (1 datoteka PDF (IX, 29 f.))
PID:
20.500.12556/DKUM-83743
UDC:
519.2+004.85(043.2)
COBISS.SI-ID:
149147651
Publication date in DKUM:
13.02.2023
Views:
2419
Downloads:
140
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:
30.01.2023
Secondary language
Language:
English
Title:
Drinking water usage forecasting using methods of time series and machine learning
Abstract:
In this thesis we focus on forecasting drinking water usage. Our main goal is to compare the ARIMA models and the LSTM neural network model. We focus on forecasting time series of individual households and time series of water supply network, which are sampled monthly. Comparison of forecasting models is performed on basis of their mean squared error when fitting the time series and predicting drinking water consumption on the test set. The results show that the ARIMA model performs best.
Keywords:
time series
,
forecasting
,
ARIMA
,
LSTM
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