Subject: Time Series Data Processing (17 - IFE213)

Basic Information

Scientific or art field:Telecommunications and Signal Processing
Course specification

Course is active from 01.10.2014..

Precondition courses

Course idMandatoryMandatory
Probability and Stochastic ProcessesYesYes
The course offers fundamental knowledge in the field of time series analysis and its applications in various domains, most notably economy, engineering as well as natural and social sciences. The students formalize the concept of a time series through notions of discrete-time signals and random processes, in order to acquire knowledge necessary to select an appropriate model for any actual time series in order to represent it compactly, analyze it as well as forecast its future behaviour.
Students will get to know examples of time series (discrete-time signals). They will be able to interpret actual time series as instances of random processes. They will master the estimation and elimination of trend and seasonal component from a time series. They will get familiar with modelling time series with the aim of their compact representation, separation into relevant components as well as forecasting future values. They will focus particularly on ARMA models and, based on the obtained knowledge, they will be able to select an appropriate model of a time series and to solve a given problem in an appropriate computational environment.
Discrete-time time series (signals), z-transform and spectrum. Random processes, stationarity and ergodicity. Introduction to modelling of time series. Estimation and elimination of trend and seasonality. Spectral analysis of time series. ARMA processes and ARMA models, modelling and forecasting with ARMA processes. Nonstationary and seasonal time series models.
The lectures are continuously followed by synchronized auditory and computer exercises. Auditory exercises contain practical problem solving sessions principally related to time series analysis and processing. In the computer lab students obtain practical experience with time series analysis software tools. Through the teaching process, students are constantly motivated to an intensive discussion, problem oriented reasoning, independent study work and active participation in the lecturing process.
Sečujski Milan, Jakovljević Nikša, Delić VladoDigitalna obrada signala2019Fakultet tehničkih nauka, Novi SadSerbian language
Sečujski Milan, Delić Vlado, Jakovljević Nikša, Radić IgorZbirka zadataka iz digitalne obrade signala2016Fakultet tehničkih nauka, Novi SadSerbian language
Milan Sečujski, Nikša Jakovljević, Vlado DelićPowerPoint prezentacije sa predavanja i on-line vežbe preko web portala Katedre za telekomunikacije i obradu signala2014Interni materijalSerbian language
Popović, M.Digitalna obrada signala1997Nauka, BeogradSerbian language
James Douglas HamiltonTime Series Analysis1994Princeton University Press, Princeton, NJEnglish
P.J.Brockwell & R.A.DavisIntroduction to Time Series and Forecasting2002SpringerEnglish
Course activity Pre-examination ObligationsNumber of points
Written part of the exam - tasks and theoryNoYes70.00
Coloquium examNoNo20.00
Name and surnameForm of classes
Missing picture!

Sečujski Milan
Full Professor

Missing picture!

Jakovljević Nikša
Associate Professor

Practical classes
Missing picture!

Jakovljević Nikša
Associate Professor

Laboratory classes