Time series have various applications in different disciplines, and they form an important class of machine learning models. This course will discuss time series models and their use in forecasting. In the course, various model fitting approaches will be studied, stationary processes will be discussed, and some Bayesian dynamic models will be introduced. Practical examples and implementations will beshown with R.