Vorlesungsverzeichnis

Suchen Sie hier über ein Suchformular im Vorlesungsverzeichnis der Leuphana.

Veranstaltungen von Apl. Prof. Dr. Deniz Karaman Örsal

Lehrveranstaltungen

Applied Time Series Forecasting (FSL) (Seminar)

Inhalt: This course offers a comprehensive, hands-on introduction to time series forecasting, blending conventional statistical techniques with modern machine learning approaches. Students will explore core forecasting concepts such as stationarity, autocorrelation, and seasonality, while gaining practical experience with statistical models like ARIMA, Exponential Smoothing. The course then transitions to machine learning methods, covering algorithms such as Random Forest, Neural Networks tailored for time-dependent data. Through real-world examples and code-along sessions, students will learn how to preprocess data, select appropriate models, validate performance, and deploy forecasts. Emphasis is placed on practical application, enabling learners to confidently tackle forecasting problems across various domains. By the end of the course, students will have built a toolkit that combines the interpretability of statistical methods with the predictive power of machine learning.