Time Series and Machine Learning

Description

The content of the lecture covers machine learning (ML) and deep learning (DL) methods for various time series applications. In particular, the following topics will be covered: - Introduction to time series analysis - Exploratory data analysis - Machine Learning for time series (e.g., supervised learning approaches like decision trees, random forest, regression models) - Deep learning for time series (e.g., recurrent neural networks (RNN) and long short-term memory (LSTM) networks) - Time series forecasting methods (e.g., regression models, transformer models) - Performance measures and model evaluation - Anomaly detection in time series - Practical implementation in Python using real-world datasets and applications The learning objectives of the course are: - Define and explain key concepts of time series - Explore and preprocess time series - Apply Machine Learning models including deep learning models to time series - Explore methods for time series forecasting - Evaluate the model performances using appropriate metrics - Investigate anomaly detection techniques for time series