This podcast episode covers a wide range of topics related to forecasting, including the challenges of COVID-19 forecasting, the importance of measuring forecasting accuracy, best practices during a pandemic, choosing the right forecasting horizon, uncertainty and causality in forecasting, building trust in forecasts, and the opportunities and threats of AI in society.
Takeaways
• Practical problem-solving and interesting mathematics are essential for good forecasting.
• Insider trading is unethical and can have legal consequences.
• Ensembles of models generally provide better forecasts than individual models.
• Probability scoring is used to measure the accuracy of probabilistic forecasts.
• Reproducibility and version control are important in data science.
• Consider the purpose of the forecast and decisions based on it when choosing the forecasting horizon.
• Forecasts should be evaluated and updated over time to account for new information.
• Causality can help identify better variables for a model, but it is not always necessary for accurate forecasts.
• Explainability is important for building trust in forecasting models.
• AI has the potential to improve our lives but also raises ethical concerns.
