Napovedovanje časovnih vrst z uporabo povratnih nevronskih mrež : diplomsko delo

Uremović, Niko

| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

First page > Show document

Show document

Title:	Napovedovanje časovnih vrst z uporabo povratnih nevronskih mrež : diplomsko delo
Authors:	ID Uremović, Niko (Author) ID Lukač, Niko (Mentor) More about this mentor... ID Bizjak, Marko (Comentor)
Files:	UN_Uremovic_Niko_2020.pdf (1,50 MB) MD5: 6CD97BDD26002FB7EFFCF6946AD03A91 PID: 20.500.12556/dkum/ab315052-ba0a-49fd-ac9d-ff5c8749f183
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V diplomskem delu predstavimo napovedovanje multivariatnih časovnih vrst z uporabo povratnih nevronskih mrež, ter primernost pristopa k napovedovanju preizkusimo na področju energetike. Za pametno krmiljenje električnih naprav je namreč nujno potrebno poznavanje posledic, ki jih imajo naše akcije na stanje naprav in njihove okolice. Stanje naprav definira več spremenljivk, zato spreminjanje stanja skozi čas opisuje multivariatna časovna vrsta. Za električno napravo grelnik vode pripravimo napovedni model, ki temelji na povratni nevronski mreži arhitekture LSTM. Ker pa se lastnosti naprave in s tem opisujoče časovne vrste lahko s časom spreminjajo, moramo za ohranjanje natančnostosti napovednega modela le-tega sproti prilagajati. V diplomskem delu predstavimo različne strategije sprotnega učenja modela in primerjamo njihovo učinkovitost na napovednem modelu za grelnik vode.
Keywords:	Multivariatne časovne vrste, napovedovanje časovnih vrst, povratne nevronske mreže, katastrofalno pozabljanje
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[N. Uremović]
Year of publishing:	2020
Number of pages:	IV, 28 f.
PID:	20.500.12556/DKUM-77070
UDC:	004.032.26:004.8(043.2)
COBISS.SI-ID:	42183427
NUK URN:	URN:SI:UM:DK:DDCO9VFU
Publication date in DKUM:	03.11.2020
Views:	2141
Downloads:	138
Metadata:
Categories:	KTFMB - FERI
:	Copy citation

Average score:	(0 votes)
Your score:	Voting is allowed only for logged in users.
Share:

Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

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:	14.08.2020

Secondary language

Language:	English
Title:	Time series forecasting by using recurrent neural networks
Abstract:	In this thesis we present the forecast of multivariate time series, by using recurrent neural networks, and test the adequacy of such approach to forecast on the field of energetics. For smart management of electric devices, it is crucial to understand the impact of our actions on the machine and its surroundings. The state of the machine is defined by multiple variables, which means the change of the state through time is defined by a multivariate time series. We prepare a forecast model for a machine water heater, basing on a recurrent neural network of the LSTM architecture. Because the characteristics of the machine, and thus the time series describing it, can change over time, it is necessary to simultaneously adapt the forecast model, to assure the accuracy of the model is preserved. In this thesis, we present different strategies for model adaptation and compare their effectiveness on the forecast model for a water heater.
Keywords:	Multivariate time series, time series forecasting, recurrent neural networks, catastrophic forgetting

Comments

Leave comment

You must log in to leave a comment.

Comments (0)

0 - 0 / 0

There are no comments!

Back