Time Series Analysis and Forecasting

1.

Subject title

Time Series Analysis and Forecasting

Анализа и предвидување на временски серии

2.

Code

m23_w_016

3.

Study program

Bioinformatics, Security, Cryptography and Coding, Cloud Computing, IT management, Еducation with ICT, Eco-informatics, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Data science in computer science and engineering, Inteligent Systems, Software for embedded systems, Software Engineering, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

ворн. проф. д-р Ефтим Здравевски проф. д-р Ѓорѓи Маџаров

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The purpose of the course is to get acquainted with the statistical and methods of machine learning for the analysis of time series and prediction. Upon completion of the course, candidates will have deep knowledge of advanced techniques and methods of time analysis and their prediction; will be able to understand, represent and analyze time series data; apply algorithms for predicting time series when solving real problems; They will be able to conceive, analyze, realize and evaluate the performance system for predicting time series.

11.

Subject content:

Analysis of linear time series, stationary and non-stationary models, transfer function models, seasonal models, box-jenkins models (autorgressive models and models of average movement). Data transformation, time series numerical team, evaluation of temporal prediction models. Trend detection and seasonal adjustment. Machine learning techniques for predicting temporal series based on neuroscience (deep learning), linear regression and models ensembles.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 60 + 45 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

60 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

45 hours

17.

Grading method

17.1.

Tests

35 points

17.2.

Seminar work / project (presentation: written and oral)

60 points

17.3.

Activities and learning

0 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

Реализирани активности

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature