Comment: This paper developed from Pesaran's Deane-Stone Lecture at the National Institute of Economic and Social Research, London. We greatly benefitted from comments when earlier versions of this paper were presented at NIESR on 21 June 2023, the International Association of Applied Econometrics Conference, Oslo 27 June 2023, and at Bayes Business School, City University 22 November 2023Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.