Analiza i prognoza vremenskih serija primjenom metoda strojnog učenja

master's thesis

• Title (english): Analysis and Prediction of Time Series Using Machine Learning Methods
• Author: Filip Klepo
• Mentor: Marin Šilić (mentor)
• Committee member: Marin Šilić (predsjednik povjerenstva)
• Committee member: Klemo Vladimir (član povjerenstva)
• Committee member: Goran Delač (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-07, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U radu se definira pojam vremenskih serija, a potom se definiraju tehnike analize i predviđanja. Koriste se ARIMA i ETS modeli koji su popularan izbor za predviđanje vremenskih serija. Za implementaciju se koristi programski jezik Python i popratne knjižnice koda za analizu, predviđanje i iscrtavanje vremenskih serija.

Abstract (english)

The thesis defines time series and introduces techniques for their analysis and forecasting. ARIMA and ETS models are chosen as forecasting methods as they are a popular choice. Implementation is done in Python programming language, using publicly available code libraries for analysis, prediction and plotting of time series.

• Keywords: vremenske serije; prognoza vremenskih serija; strojno učenje; analiza vremenskih serija; ARIMA; ETS
• Keywords (english): time series; time series prediction; machine learning; time series analysis; ARIMA; ETS
• Language: croatian
• URN:NBN: urn:nbn:hr:168:079219
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 10:10:17