Predviđanje dnevnog broja aktivnih covid-19 slučajeva pomoću analize vremenskih serija

master's thesis

• Title (english): Covid-19 Active Daily Cases Prediction Using Time Series Analysis
• Author: Ivona Mihotić
• 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: 2021-07-05, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U ovom radu opisane su metode analize i predviđanja vremenskih serija. Detaljno su opisani ARIMA i Prophet model, koji su korišteni za predviđanje aktivnih COVID-19 slučajeva u državama: Sjedinjene Američke Države, Brazil i Francuska. Nadalje, prikazan je i objašnjen postupak implementacije navedenih modela. Za evaluaciju modela, korišteno je predviđanje u jednom i više koraka. U smislu mjera vrednovanja za procjenu prediktivne snage svakog od pristupa korišteni su RMSE i MAPE. Njima je ispitana uspješnost ostvarenog predviđanja te su prikazani i opisani rezultati ispitivanja.

Abstract (english)

This thesis describes methods of time series analysis and prediction. The ARIMA and Prophet models, that are used to predict active COVID-19 cases in the countries: United States, Brazil, and France, are described in detail. Furthermore, the implementation of these models is presented and explained. To evaluate these models, one step and multi-step forecasting were used. Evaluation measures used to assess the predictive power of each of the approaches were RMSE and MAPE. They examined the success of the achieved prediction and presented and described the test results.

• Keywords: ARIMA; Prophet; COVID-19; vremenska serija; analiza vremenskih serija; metode predviđanja vremenskih serija
• Keywords (english): ARIMA; Prophet; COVID-19; time series; time series analysis; time series prediction methods
• Language: croatian
• URN:NBN: urn:nbn:hr:168:398631
• 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 10:26:08