What do the weather and the stock market have in common? If your predictions are wrong, you may get burned. But more relevant to the topic at hand, both are examples of time series data: the order in which the data points present themselves is essential to understand what’s going on. Using specialized time series analysis techniques, we can spot trends in the data and build an accurate forecasting model.
This course provides an overview of time series analysis methods, combining theory and practice. You will learn about basic concepts like trends, seasonality, and stationarity, and work with popular models that implement those. You will also learn about more advanced concepts like non-parametric models and generalized additive models. At the end of the course, you will be able to build forecasting models for a wide variety of domains and problem sets.