Master 2025/2026

Time Series Analysis and Forecasting

Type: Compulsory course (Master of Data Science)

Delivered by: Big Data and Information Retrieval School

When: 2 year, 2 module

Open to: students of one campus

Language: English

Course Syllabus

Abstract

The course explores the principles and methodologies of time series analysis and forecasting, with a strong focus on practical applications in business contexts. Such topics as seasonality, trends, stationarity and autocorrelation, using business-oriented examples like demand forecasting, sales analysis and financial modeling will be covered. The course will introduce statistical and econometric models, including ARIMA and MSTL, and advanced machine learning approaches, including Prophet.A special emphasis will be placed on the use of external regressors - such as economic indicators, marketing activity and competitor data - to enhance model accuracy and incorporate real-world influences. Students will learn to integrate these regressors into their forecasting models, understanding their impact on business performance metrics and outcomes.