How to Examine and Predict Time Series: Methods and Applications

2025/2026

Academic Year

ENG

Instruction in English

6

ECTS credits

Delivered by:

School of Data Analysis and Artificial Intelligence

Type:

Mago-Lego

When:

1, 2 module

Instructor

Tomashchuk, Korney

Course Syllabus

Abstract

The course is dedicated to modern methods of time series analysis and forecasting. Students will get familiar with both classical approaches and advanced topics: machine learning, analysis of chaotic systems, and fractal analysis. Particular attention is paid to the analysis and forecasting of chaotic time series.

Learning Objectives

• To form a systematic understanding and practical skills in students for working with time series of various natures, including complex chaotic and fractal times series, from the stage of preprocessing to building and verifying predictive models.

Expected Learning Outcomes

• Decompose a series into components, check for stationarity, build and interpret ACF/PACF
• Apply filtering methods to highlight trend and suppress noise. Justify the choice of filtering method
• Select parameters and build ETS, ARIMA, GARCH models. Evaluate forecast quality.
• Design, train, and evaluate models for forecasting and classifying time series
• Distinguish chaotic and regular time series. Analyze and forecast chaotic time series.
• Conduct R/S analysis and DFA, calculate and interpret the Hurst exponent
• Possess methods for preprocessing and filtering time series
• Build and interpret classical forecast models
• Apply machine learning methods for forecasting and classifying time series
• Conduct analysis and forecast chaotic time series using nonlinear dynamics methods
• Conduct fractal analysis of a time series

Course Contents

• Introduction and Fundamental Concepts
• Filtering and Smoothing
• Classical Forecasting Methods
• Machine Learning for Forecasting and Classifying Time Series
• Analysis and Forecasting of Chaotic Time Series
• Fractal Analysis of Time Series

Assessment Elements

• L1
• L2
• L3
• L4
• E1
• E2

Interim Assessment

• 2025/2026 2nd module
  0.2 * E1 + 0.2 * E2 + 0.15 * L1 + 0.15 * L2 + 0.15 * L3 + 0.15 * L4

Bibliography

Recommended Core Bibliography

• Современные проблемы нелинейной динамики, Малинецкий, Г. Г., 2000

Recommended Additional Bibliography

• Nonlinear time series analysis, Kantz, H., 2000

Authors

• Антропова Лариса Ивановна
• GROMOV Vasilii ALEKSANDROVICH