Applied Forecasting: From Linear Models to Data Mining and Deep Learning

Catalog description

Embark on an exciting journey to master real-world forecasting projects. This course equips you with essential statistical and computational tools crucial for success in the field. You will explore both classical and modern forecasting methods. Engage with logistic regression and decision trees for classification tasks, such as credit risk assessment. Delve into linear and nonlinear prediction models like ARx and feedforward neural networks for analyzing high and low-frequency time series. We will focus on variable selection, estimation, and combination techniques. Learn how to evaluate forecasts from both statistical and user perspectives. Understand the critical role of hyperparameters, including autoregressive model orders and neural network configurations. Throughout the semester, you will apply your knowledge in a hands-on forecasting project. This practical experience will solidify your understanding and prepare you for real-world challenges. Join us to elevate your forecasting skills and make a significant impact in your field!