Online prediction for streaming observational data

The automated collection of streaming observational data has become standard and defies most traditional analytic techniques. It is not just that models are hard to identify, there may not be any model that can be safely and usefully assumed. Indeed, frequently it is only predictions that can be made and assessed. Problems for this kind of data are often called M-Open and have motivated new statistical approaches and philosophies. This paper will review some of the most successful recent predictive methods for the M-Open problem class. Techniques include predictors from Bayesian nonparametrics such as Gaussian process priors, predictors using expert advice such as the Shtarkov solution, hash function based predictors such as the Count-Min sketch, conformal prediction, and neural nets including Long Short-Term Memory networks.