Time Series Analysis & Forecasting

In this course, you will learn the essentials to perform time series analyses. We start with an introduction where we discuss the omnipresence of time series in data science and lay out the course objectives. Secondly, we discuss the specificities of time series data and models, including the key concept of stationarity. Next, we zoom in on a descriptive analysis of time series data. Tools such as the correlogram, filtering and smoothing will aid you in exploring data properties at the beginning of your time series analysis. The fourth part of the course then introduces indispensable tools for modeling single stationary time series, namely the flexible and popular family of AutoRegressive Moving Average (ARMA) models. This is followed by a section on forecasting and evaluating forecast performance. In part six of the course, we turn to the universe of non-stationary time series, providing a discussion on trends, unit roots and the need for unit root tests as an essential part of any time series analysis. Next, we turn from univariate time series models to multivariate time series models where we discuss how to jointly model the dynamics between several time series. We extensively discuss both stationary multivariate time series models such as Vector AutoRegressions as well as non-stationary time series models such as Vector Error Correction Models thereby equipping you with the necessary tools to perform cointegration analysis. Lastly, we provide some initial pointers on how to handle “Big” time series data sets, thereby reporting upon our own research contributions. The course concludes with an overview of all covered topics and provides an outlook on other interesting, more advanced time series topics.

The course combines methodological and technical insights with practical implementation details and code examples on how to use R for time series analysis and forecasting. We hereby focus on applications, how to interpret your results and how to avoid common pitfalls in empirical research.

The course features around 5 hours of video lectures , various multiple choice questions, and references to background literature. A certificate signed by the instructors is provided upon successful completion.