Course description

This class is intended to present a wide range of forecasting tools. Tentative list of topics that will be discussed in this class are listed below:

  • Time series models: A broad range of “classical” models such as autoregressive models, ARMA, ARIMA, GARCH, … and their extensions to add external information (dynamic regression)
  • Neural Networks: An introduction to neural networks for time serie data
  • Model selection: Different ways to assess the performance of a model using back-testing, cross-validation, bagging, information criterion
  • Model combination: Combining different forecast startegies and assess them
  • Judgmental forecasting: Making forecast in the absence of historical data
  • Web scraping: Automatic extraction of data from websites using SelectorGadget, rvest and quantmod
  • Data visualizations: Exploratory data analysis with Base R and ggplot2
  • Reporting: Performances reporting

The accent is made on practical aspects of forecasting and case studies will be presented on several occasions. Students will be required to participate in groups to “forecasting competitions”. Familiarity with a programming language is assumed. Within this class, we will use the statistical language R, but the students are welcome to use another programming language as long as it allows them to complete the different tasks of this class.

This course is the sequel of the Time Series and Forecasting class taught in Spring 2018. Although not mandatory, we strongly recommend the students to follow the Time Series and Forecasting class prior to ours as it will facilitate they learning curve and diminish the importance of the workload that this class represents.