Predictive Consistency: Sequential Forecasting Systems

This lecture by the instructor covers the concept of predictive consistency in sequential forecasting systems. It discusses the distinction between prediction and estimation, highlighting the ease of predicting future values even when parameters are unidentifiable. The lecture explores Gaussian processes, prequential (predictive sequential) setups, and the importance of probability forecasting systems. It delves into prequential consistency and out-of-model performance, emphasizing the challenges of using models for data generated from unknown distributions. The lecture also touches on online statistical learning theory, learnability, and prediction with expert advice. It concludes by emphasizing the utility of prediction over estimation and the significance of prequential approaches in evaluating forecasting systems.