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undergraduate thesis
Predikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:223265
Mutvar, Lovro
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Mutvar, L. (2021). Predikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:223265

Mutvar, Lovro. "Predikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:223265

Mutvar, Lovro. "Predikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:223265

Mutvar, L. (2021). 'Predikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:223265

Mutvar L. Predikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 July 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:223265

L. Mutvar, "Predikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:223265

Metadata
TitlePredikcija cijena vrijednosnica zasnovana na Dynamic Time Warping udaljenosti
Title (english)Prediction of Asset Prices Based on Dynamic Time Warping Distance
AuthorLovro Mutvar
MentorZvonko Kostanjčar (mentor)
Committee memberZvonko Kostanjčar (predsjednik povjerenstva)
Committee memberLovre Mrčela (član povjerenstva)
Committee memberBruno Gašperov (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-02, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada bila je predikcija smjera kretanja cijena dionica na temelju Dynamic Time Warping udaljenosti vremenskih nizova. Ulazni podaci bili su cijene 368 dionica iz S&P (Standard & Poor's) indeksa u razdoblju od 3.6.2013. do 6.4.2017., ukupno 970 radnih dana. U okviru ovog rada implementirani su i detaljno objašnjeni klasifikacijski algoritmi strojnog učenja: algoritam k – najbližih susjeda (engl. k – nearest neighbours, KNN) i algoritam stroja potpornih vektora (engl. Support vector machine, SVM). Navedeni algoritmi klasificiraju vremenske nizove na temelju njihove udaljenosti dobivene pomoću DTW algoritma. Klasifikacijski modeli trenirani su i testirani na ulaznim podacima te su primijenjene različite metrike za evaluaciju njihove uspješnosti. Kao i što je očekivano algoritam SVM sa univarijatim nizovima dao je bolje rezultate od algoritma KNN. Unatoč očekivanjima, SVM sa multivarijatnim nizovima, koji koristi više informacija te u obzir uzima cijene svih dionica u određenom trenutku, pokazao se kao lošiji model za predikciju od SVM-a sa univarijatnim nizovima.
Abstract (english)
The aim of this paper was to predict the direction of stock price movement based on Dynamic Time Warping distance of time series. The input dataset were prices of 368 shares from the S&P (Standard & Poor's) index in the period from 3.6.2013. to 6.4.2017, which is a total of 970 working days. Within this paper, two machine learning classification algorithms have been implemented and explained in detail: the k - nearest neighbor algorithm (KNN) and the Support vector machine (SVM) algorithm. These algorithms classify time series based on their distance obtained using the DTW algorithm. Classification models were trained and tested on input data and various metrics were applied to assess their performance. As expected, the SVM algorithm with univariate time series gave better results than the KNN algorithm. Despite expectations, an SVM with multivariate time series, which uses more information and takes into account all stock prices at a given time, has proven to be a poorer model for predicting price movements than the SVM algorithm with univariate time series.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:223265
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
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Created on2023-01-14 21:16:44