MK EN
ФИНКИ

Subject

Introduction to Time Series Analysis

1. Course Title Introduction to Time Series Analysis
Introductio to time series analysis
2. Code F23L3W076
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) First Cycle
6. Academic year / semester 7 / Winter
7. Number of ECTS credits 6
8. Teacher Ефтим Здравевски
9. Prerequisites for enrolling in the course Artificial Intelligence or Introduction to Data Science or Machine Learning
10. Objectives of the course programme (competences) Запознавање на студентите со анализа на произволни временски серии со традиционални статистички методи, како и со методи базирани на длабоко учење. Курсот дава вовед во типовите на временски серии, покрива стационарни процеси, ARMA модели, ARIMA и сезонални ARIMA модели, временско-просторни методи. Со знаењето стекнато на курсот студентите ќе може да анализираат временски серии од разновидни извори, податочни текови (data streams), IoT и да откриваат трендови и аномалии, да предвидуваат идни појави, како и да ги користат за препознавање на разновидни настани кои се опишани со временски серии.
11. Course content Lectures:
1. Карактеристики на временски серии
2. Корелација и автокорелација
3. Регресија на временски серии и истражувачка анализа на податоци
4. ARIMA модели
5. Спектрална анализа и филтрирање
6. Екстракција на карактеристики од временски домен и статистички методи во фреквентен домен
7. Инженерство и генерирање на атрибути од временски серии
8. Откривање на аномалии
9. Откривање на концептуални промени
10. Моделирање на целни променливи за препознавање на тековни настани и предвидување на идни настани
11. Употреба на различни архитектури на длабоки невронски мрежи за анализа на временски серии
12. Употреба на различни методи за учење во реално време
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 30 + 45 + 15 + 15 + 75 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 30 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 45 hours
16. Other forms of activities
16.1. Project assignments 15 hours
16.2. Independent assignments 15 hours
16.3. Home study 75 hours
17. Assessment method
17.1. Tests 10 points
17.2. Seminar paper / project (presentation: written and oral) 15 points
17.3. Activities and learning 10 points
17.4. Final exam 70 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. Robert H. Shumway David S. Stoffer | Time Series Analysis and Its Applications | Springer | 2015
2. Brockwell, Peter J., Davis, Richard A. | Introduction to Time Series and Forecasting | Springer | 2016
3. Douglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci | Introduction to Time Series Analysis and Forecasting, | John Wiley & Sons, Inc. | 2015
4. Joos Korstanje | Advanced Forecasting with Python: With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR | Apress | 2021
5. Jason Brownlee | Deep Learning for Time Series Forecasting Predict the Future with MLPs, CNNs and LSTMs in Python | Machine Learning Mastery | 2020
6. Ivan Gridin | Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions | BPB Publications | 2021
7. Aileen Nielsen | Practical Time Series Analysis: Prediction with Statistics and Machine Learning | O`Reilly | 2019
8. Francesca Lazzeri | Machine Learning for Time Series Forecasting with Python | Wiley | 2020
9. Ben Auffarth | Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods | Packt | 2021
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects