ECTS: 3
Course description:
Introduction – basic concepts of time-series. Common time-series models (linear, autoregressive, ARMA, ARIMA, etc.). Forecasting with Neural Networks (e.g., LSTM models). Selected advanced methods (e.g., Facebook’s Prophet). Forecasting validation and quality measures. Data Science challenge (Kaggle). Lab hours with R, Python (scikit-learn), TensorFlow (Keras), PyTorch.
Course coordinator: Prof. Aggelos Pikrakis