Master 2024/2025

How to Examine and Predict Time Series: Methods and Applications

Type: Elective course (Data Science)

Area of studies: Applied Mathematics and Informatics

Delivered by: School of Data Analysis and Artificial Intelligence

Where: Faculty of Computer Science

When: 2 year, 1, 2 module

Mode of studies: offline

Open to: students of one campus

Instructors: Korney Tomashchuk

Master’s programme: Data Science

Language: English

ECTS credits: 6

Course Syllabus

Abstract

How to forecast rates of a national currency? How to identify an oncoming heart attack in good time? – the answers to these questions are associated with the problems (1) to predict a chaotic time series and (2) to reveal typical sequences in an observed time series, respectively. All these problems along with many others comprise the field of time series prediction. The course combines real-world applications with a strong theoretical background: the authors selected mathematical topics required to solve complex problems of actual practice. On the other hand, several topics focus on “mathematics of future” that is theories that will have become the basis of applications in the decades to come. The course starts with simple concepts and gradually works in more advanced applications. To be specific, the course deals with main models to examine and predict regular time series (exemplified by ARIMA and GARCH models), chaotic time series (predictive clustering, constructive neural networks, deep learning models and others) as well as with state-of-the-art approaches used to distinguish regular and chaotic time series, using observations of the time series at hand only; particular topics deal with the concepts of forecasting (time) and stationarity horizons. Applications considered range from econometrics problem to mobile health.