Learning to Forecast: The Probabilistic Time Series Forecasting Challenge

被引：0

作者：

Bracher, Johannes ^{[1
,2
]}

Koster, Nils ^{[1
]}

Krueger, Fabian ^{[1
]}

Lerch, Sebastian ^{[1
,2
]}

机构：

[1] Karlsruhe Inst Technol, Karlsruhe, Germany

[2] Heidelberg Inst Theoret Studies, Heidelberg, Germany

来源：

AMERICAN STATISTICIAN | 2024年 / 78卷 / 01期

关键词：

Model evaluation and selection; Prediction; Quantile modeling; Statistics education; Time series analysis; PREDICTION;

D O I：

10.1080/00031305.2023.2199800

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：115 / 127

页数：13

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