Time Series Analysis Course

Abstract

The Time Series Analysis course aims to provide a hands-on introduction to analyzing time series data using Python, covering foundational principles, analytical methods, and predictive techniques. This includes stationarity, autocorrelation, multivariate dependencies, forecasting methods, and an introduction to machine learning and deep learning technics such as RNNs and LSTM models. The course integrates theory with practical application, enhancing skills in time series analysis for both academic research and professional development. The course is designed for research students—Master’s, PhD, and postdoctoral students from all faculties who are looking to deepen their expertise in time series analysis using Python.
Introduction to practical time series analysis, covering foundational principles, common analytical methods, and advanced topics, including machine learning and deep learning approaches. Designed for students with a basic understanding of statistics and data science, the course combines frontal teaching, class exercises and home practice.