MK EN
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Subject

Time-Series Analysis and Forecasting

1. Course Title Time-Series Analysis and Forecasting
Time Series Analysis and Forecasting
2. Code m23_w_016
3. Study Programme —
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) Second cycle
6. Academic year / semester 9 / Winter
7. Number of ECTS credits 6
8. Teacher Eftim Zdravevski, Georgi Madjarov
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) The goal of the course is for students to become familiar with statistical and machine learning methods for time series analysis and forecasting. Upon completion of the course, candidates will have in-depth knowledge of advanced techniques and methods for time series analysis and forecasting; will be able to understand, represent, and analyze time series data; apply time series forecasting algorithms to solve real-world problems; and design, analyze, implement, and evaluate the performance of a time series forecasting system.
11. Course content Analysis of linear time series, stationary and nonstationary models, transfer function models, seasonal models, Box-Jenkins models (autoregressive models and moving average models). Data transformation, numerical representation of time series, evaluation of time series forecasting models. Trend detection and seasonal adjustment. Machine learning techniques for time series forecasting based on neural networks (deep learning), linear regression, and ensembles of models.
12. Learning methods Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 60 + 0 + 45 + 60 + 45 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 60 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 0 hours
16. Other forms of activities
16.1. Project assignments 60 hours
16.2. Independent assignments 45 hours
16.3. Home study 45 hours
17. Assessment method
17.1. Tests 35 points
17.2. Seminar paper / 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 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam Activities carried out
20. Language of instruction Macedonian and English
21. Method for monitoring the quality of teaching internal evaluation and survey mechanism
22. Literature
22.1. Required literature
1. George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel | Time Series Analysis: Forecasting and Control, 5th Edition | John Wiley & Sons, Inc. | 2015
2. Søren Bisgaard and Murat Kulahci | Time Series Analysis and Forecasting by Example | John Wiley & Sons, Inc. | 2011
3. Witold Pedrycz, Shyi-Ming Chen | Time Series Analysis, Modeling and Applications: A Computational intelligence perspective | Springer | 2013
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects