MSE Master of Science in Engineering

The Swiss engineering master's degree

Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories:

12-15 ECTS in technical scientific modules (TSM)
TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
9-12 ECTS in fundamental theoretical principles modules (FTP)
FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
6-9 ECTS in context modules (CM)
CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

Analysis of Sequential Data (TSM_AnSeqDa)

This course provides a comprehensive introduction to time series analysis, covering both classical statistical methods and modern machine learning approaches. Starting with foundational concepts in probability and stationarity, students learn to model temporal dependencies through autocorrelation structures and classical models (MA, AR, ARMA, ARIMA, SARIMA). The curriculum progresses to practical forecasting techniques including smoothing and regression methods, volatility modeling for financial applications (ARCH/GARCH), and frequency-domain analysis through spectral methods. Advanced topics include Kalman filtering for recursive state estimation and deep learning architectures for time series. The course emphasizes both theoretical understanding and practical applications across domains such as finance, economics, and signal processing.
The labs are done using Python

Theoretical Understanding:

Understand fundamental concepts of time series analysis including stationarity, autocorrelation, and temporal dependence structures
Master classical time series models (MA, AR, ARMA, ARIMA, SARIMA) and their mathematical foundations
Comprehend volatility modeling frameworks (ARCH/GARCH) and their applications in financial contexts
Grasp frequency-domain analysis through spectral methods and Fourier transforms
Understand state-space models and recursive filtering through Kalman filtering theory

Practical Skills:

Identify and characterize temporal patterns in real-world data using autocorrelation and partial autocorrelation functions
Select, estimate, and validate appropriate time series models for different data characteristics
Apply smoothing techniques and build forecasting models using both classical and regression-based approaches
Implement volatility models for financial risk assessment and market analysis
Utilize spectral analysis tools to detect periodic components and frequency patterns
Apply Kalman filtering for prediction and correction in dynamic systems
Leverage deep learning architectures for complex time series prediction tasks

Applied Competencies:

Conduct end-to-end time series analysis projects from data exploration to model deployment
Critically evaluate model performance and select appropriate methods based on data properties and objectives
Interpret and communicate analysis results to technical and non-technical audiences
Apply learned techniques across various domains including finance, economics, engineering, and data science

Getting Started: overview of time series data, objectives of analysis, and course organization.
Basic Statistics and Probability (review): random variables, expectations, correlations, stationarity.
Correlations and MA Processes: autocorrelation, partial autocorrelation, and moving average models.
AR, ARMA, ARIMA, SARIMA Processes: autoregressive structures, seasonal extensions, model selection.
Smoothing, Prediction, and Regression: moving averages, exponential smoothing, linear and nonlinear regression approaches.
Volatility Models: ARCH, GARCH, and extensions; applications in financial time series.
Spectral Analysis: Fourier methods, frequency-domain approaches, and applications.
Kalman Filtering: Recursive structure: prediction and correction, interpretation.
Deep Learning for Time Series and selected topics.

Slides will be available covering the topics of the course.

In addition, recommended books are:

R.H.Shumway and D.S. Stoffer, Time Series Analysis and Its Applications, Springer 2017
François Chollet, Deep Learning with Python, 3rd edition, Manning Publications Co., 2025 (https://www.manning.com/books/deep-learning-with-python)
R. Hyndman and G. Athanasopoulos., Forecasting: Principles and Practice, Springer, 2018 (online free textbook at otexts.com/fpppy/, 2025 version)