Learning to forecast: The probabilistic time series forecasting challenge

TL;DR

This paper discusses a course-based approach to teaching probabilistic time series forecasting through real-time student submissions of forecasts for weather and financial data, highlighting practical skills and empirical insights.

Contribution

It introduces a novel educational format for probabilistic forecasting that combines real-time data prediction with practical evaluation and feedback.

Findings

Students' forecasts show diverse probabilistic skills.

Empirical analysis reveals forecast performance patterns.

Lessons learned inform future forecasting education.

Abstract

We report on a course project in which students submit weekly probabilistic forecasts of two weather variables and one financial variable. This real-time format allows students to engage in practical forecasting, which requires a diverse set of skills in data science and applied statistics. We describe the context and aims of the course, and discuss design parameters like the selection of target variables, the forecast submission process, the evaluation of forecast performance, and the feedback provided to students. Furthermore, we describe empirical properties of students' probabilistic forecasts, as well as some lessons learned on our part.